Proposed MS and PhD Research Program Area in Machine Learning and Data Science

ACADEMIC SENATE: SAN DIEGO DIVISION, 0002 UCSD, LA JOLLA, CA 92093-0002 (858) 534-3640 FAX (858) 534-4528 April 18, 2018 PROFESSOR TRUONG NGUYEN, Chair Department of Electrical and Computer Engineering PROFESSOR ALON ORLITSKY Department of Electrical and Computer Engineering SUBJECT: Proposed MS and PhD Research Program Area in Machine Learning and Data Science At its April 9, 2018 meeting, the Graduate Council approved the Department of Electrical and Computer Engineering s proposal to add a new research program area in Machine Learning and Data Science to its MS and PhD degree programs. Thank you for providing the Council with additional information about the proposed curriculum and the letters of support from departments with overlapping content. The Council will forward the Department s request to establish new MS and PhD degree titles in Electrical Engineering (Machine Learning and Data Science) for placement on the June 5, 2018 Representative Assembly agenda for final approval. If approved, the new degree title will be available to students in Fall Quarter 2018. The catalog copy describing the Machine Learning and Data Science Research Program was approved and will be included in the fall update of the 2018-19 General Catalog. Sincerely, Sorin Lerner, Chair Graduate Council cc: F. Ackerman M. Allen R. Horwitz C. Lyons K. Pogliano R. Rodriguez S. Yadegari

Title A proposal to add a new major field of study in Machine Learning and Data Science to the M.S. and Ph.D. degrees available within the Electrical and Computer Engineering Department, effective Fall 2018. Date of Preparation October 6, 2017 Contact Information Professor Alon Orlitsky Electrical and Computer Engineering Dept. 9500 Gilman Drive #0407 La Jolla, CA, 92093 aorlitsky@eng.ucsd.edu 858-822-0228

Executive Summary We propose to establish a graduate program of study in Machine Learning and Data Science to the M.S. and Ph.D. degree offerings within the Electrical and Computer Engineering Department in the Jacobs School of Engineering, with an effective date of Fall 2018. Students who complete the program will receive the M.S. or Ph.D. in Electrical and Computer Engineering (Machine Learning and Data Science). Designed for graduate students with diverse undergraduate degrees, the program will span the spectrum from fundamental theory to practical applications. It will quickly bring students up to speed with the field s mathematical and computational foundations, continue with state of the art machine-learning and algorithmic tools that undergird today s big-data analytics, and offer specialized courses that bridge the field with important branches of science and engineering. Several courses in the proposed program are already being taught in the department. A number of new courses have been proposed and are currently in the ecourse review process, while others are currently being offered as one-time special-topic courses that will be proposed as new courses in the near future. The proposed program will increase the strength and visibility of our department s existing efforts in data analytics and computation. It will attract new students interested in the rapidly growing data-science field, and will add to our research and fund raising capabilities in this important field. Section 1. Introduction 1. Aims and objectives of the program. Data has become central to our daily lives and there is growing demand for professionals with data analysis skills. The goal of this program is to give students background and preparation for employment and research in areas related to Machine Learning and Data Science. Core coursework in this program will place an emphasis on building strong mathematical and computational foundations (linear algebra, statistics and programming), and students will be able to delve into both theory and practical applications. Computing and data play an ever-growing role in all areas of human knowledge. There are potential career paths in a wide variety of businesses looking to use data effectively, as well as in government agencies, academia and health care. In recognition of this strong and imminent need, the ECE Department is proposing this new graduate program to create a talent pool that is trained in Machine Learning and Data Science. We anticipate that the student population in Machine Learning and Data Science will closely follow that of the broader Electrical and Computer Engineering Department, including many out of state and international students. While many incoming students will have a BS in electrical engineering, graduates of other engineering majors are expected as well. Prior work experience is not required, although a few students may return from industry to pursue a higher degree in this area. 2. Historical development of the field and historical development of departmental strength in the field. The Electrical and Computer Engineering Department already has significant strength in Machine Learning and Data Science. A recent survey, included in section 3.5, shows 21 ECE faculty actively working in these areas. However, the lack of an official program in these topics substantially diminishes the awareness of these efforts among both current and prospective students. Creating the program will raise our visibility and likely increase both the quality and quantity of applicants to the department. We would like prospective students to be able to specifically select Machine Learning and Data Science as their major when they apply to the ECE Department, and subsequent to their arrival here, take courses centering around and designed specifically for this field.

3. Timetable for development of the program, including enrollment projections. The program is ready to go now. A group of professors within the Electrical and Computer Engineering Department is already working in this area, and many of the necessary classes are already being taught. Other classes are currently being taught as Special Topics courses and we are in the process of submitting proposals for them to become new courses. We would like to make Machine Learning and Data Science an official program within the department to which students can apply, and allow them to focus specifically in this area. Students already enrolled in another ECE major will have the option of petitioning to transfer into this new major. The new program will not reduce the overall enrollment within the department, and will likely increase enrollment due to increased visibility and the high demand for a formal program in this area of study. This demand can be observed from current students who overwhelmingly choose machine-learning related courses, as well as from student applicants who ask about the availability of such courses. 4. Relation of the proposed program to existing programs on campus and to the Campus Academic Plan. This program is a natural development for the ECE Department. As noted above, a number of faculty members are focused on this area already: they teach classes in Statistical Learning, Big Network Data, Probability and Statistics for Data Science, image processing, and bio-informatics, to name a few, and their graduate students already do research in these areas. The lack of an official program in Machine Learning and Data Science reduces the visibility of our strength in this area to prospective students, and thus likely dissuades some excellent students from applying to our department. By creating an official program in this area, we can increase the quality and quantity of students who apply to the department. 5. Interrelationship of the program with other University of California institutions, if applicable. UC Berkeley offers Master of Information and Data Science in the School of Information UC Davis no specific graduate program in Machine Learning and Data Science, but both are areas of active research in the Computer Science Department UC Irvine no specific graduate program in Machine Learning or Data Science UCLA offers Master of Science in Engineering With Certificate of Specialization in Data Science Engineering UC Merced no specific graduate program in Machine Learning and Data Science UC Riverside no specific graduate program in Machine Learning and Data Science, but Data Science is an area of active research in the Computer Science and Engineering Department UC San Diego Currently proposing program in this area (this proposal) UC San Francisco no specific program in Machine Learning and Data Science UC Santa Barbara no specific program in Machine Learning and Data Science, but both are areas of research in the Computer Science Department UC Santa Cruz no specific program in Machine Learning and Data Science, but topics are covered in several departments In addition, a number of non-uc schools, including Stanford, MIT, Columbia, GeorgiaTech, and the University of Michigan, offer Masters and Ph.D. programs in data science. 6. Department or group which will administer the program. The program will be administered by the Electrical and Computer Engineering Department. The process for administering the program is already in place as students are already taking many of the classes required for this major. The Machine Learning and Data Science program will be managed in the same way as other fields within the department, including degree planners, procedures for exams and thesis, etc. Other existing fields within the ECE department include those listed below. Note that the department is currently proposing to eliminate three of these areas, and will be submitting a separate proposal to the Graduate Council with those requests. The new major field

of Machine Learning and Data Science will complement several of our current areas, particularly Intelligent Systems, Robotics, and Control. 1. Applied Ocean Sciences (in process of eliminating) 2. Applied Optics and Photonics 3. Communication Theory and Systems 4. Computer Engineering 5. Electronic Circuits and Systems 6. Applied Physics - Electronic Devices and Materials 7. Intelligent Systems, Robotics, and Control 8. Magnetic Recording (in process of eliminating) 9. Applied Physics - Space Science (in process of eliminating) 10. Signal and Image Processing 11. Nanoscale Devices and Systems 12. Medical Devices and Systems Section 2. Program 1. Undergraduate preparation for admission. Students will have the same admission requirements as for the rest of the Electrical and Computer Engineering Department. Prospective graduate students apply to a specific major within ECE, and applications to the Machine Learning and Data Science major will be reviewed with particular attention placed on performance in courses related to machine learning, data science, computation, and statistics. 2. Foreign language. There will be no foreign language requirement that is specific to Machine Learning and Data Science. If the Electrical and Computer Engineering Department adopts a foreign language requirement in the future, that requirement will flow down to this program. 3. Program of study: a. Specific fields of emphasis Currently, each ECE specialty area has its own individual set of competency course requirements, but all majors, including Machine Learning and Data Science, have the same total credit requirement as described below: (a) M.S. degree: Complete 48 academic units of which at least 36 units must be in graduate courses. Among the 48 units, ECE 299 (graduate research) cannot be counted for more than 8 units for Plan I or more than 4 units for Plan II. Students in Plan I must submit an M.S. thesis and pass the oral defense of the thesis, and students in Plan II must pass a written comprehensive examination. (b) Ph.D. degree: Complete 48 academic units of which at least 36 units must be in graduate courses. ECE 299 (graduate research) cannot be counted for more than 8 units. To receive the Ph.D. degree, students must pass the Preliminary Examination, the University Qualifying Examination (advancing to candidacy), and complete and defend the Ph.D. dissertation. The focus of the program in Machine Learning and Data Science is embodied in a combination of existing and new courses offered by the ECE Department. Broadly speaking, there will be four emphasis areas: 1. Core: Basic mathematical courses needed for further learning of the field - linear algebra, machine learning, probability and statistics, and programming. 2. Analytics: Advanced mathematical and statistical tools for modeling and understanding data.

3. Computation: algorithms, distributed processing, and hardware platforms for large-scale machine learning. 4. Applications: machine learning and data science applications to diverse fields such as robotics, bioinformatics, and networking. b. Plan(s): Master s I (with thesis) Master s II (with comprehensive exam) Doctor s A (5-member committee, mandatory oral defense) c. Unit requirements The following 12 courses (48 units) will be required for this major. 4 courses/16 units core courses: ECE 143 (currently ECE 188, Special Topic) - Programming for Data Analysis (instructor varies) ECE 269 - Linear Algebra (Piya Pal) ECE 271A - Statistical Learning I (Nuno Vasconcelos) ECE 225A (currently ECE 289, Special Topic) - Probability and Statistics for Data Science (Alon Orlitsky) 4 additional courses/16 additional units (with at least one course in each area) from: Analytics ECE 271B - Statistical Learning II (Nuno Vasconcelos) ECE 273 - Convex Optimization and Applications (Piya Pal) ECE 275A - Parameter Estimation (Kenneth Kreutz-Delgado) Computation ECE 289, Special Topic - Optimization and Acceleration of Deep Learning on Various Hardware Platforms (Farinaz Koushanfar) ECE 289, Special Topic - Parallel Processing in Data Science (Siavash Mirarab) ECE 289, Special Topic - Scalable Learning (Bill Lin) Applications ECE 208 - Computational Evolutionary Biology (Siavash Mirarab) ECE 209 (currently ECE 289, Special Topic) - Statistical Learning for Biosignal Processing (Vikash Gilja) ECE 227 (currently ECE 289, Special Topic) - Big Network Data (Massimo Franceschetti) ECE 228 (currently ECE 289, Special Topic) - Machine Learning for Physical Applications (Peter Gerstoft) ECE 268 - Security of Hardware Embedded Systems (Farinaz Koushanfar) ECE 271C - Deep Learning and Applications (Nuno Vasconcelos) ECE 276A - Sensing and Estimation in Robotics (Nikolay Atanasov) ECE 276B (currently ECE 289, Special Topic) - Planning and Learning in Robotics (Nikolay Atanasov) ECE 276C (currently ECE 289, Special Topic) - Advances in Robotics Manipulation (Michael Yip) 4 courses/16 units Technical Electives: Any 4 unit, 200+ course from ECE, CSE, MAE, BENG, CENG, NANO, SE, MATS, MATH, PHYS or CogSci taken for a letter grade may be counted. Exceptions to this list require departmental approval. Up to 12 units of undergraduate ECE coursework (ECE 111+ only; and not including ECE 195, 197, 198 or 199) may be counted. MS students (Plan II) are allowed no more than 4 units of ECE 299 (research units) as technical electives. PhD and MS students (Plan I) are allowed no more than 8 units of ECE 299 as technical electives. d. Required and recommended courses, including teaching requirement

As listed above. There is no specific teaching requirement. e. (For Master s Plan II only) Description of capstone element The capstone element will be a comprehensive exam. Questions and evaluation methods will be consistent with the rest of the ECE department. f. Licensing requirements N/A No licensing requirements are involved in this field. 4. Field examinations oral. The preliminary exam will be consistent with the rest of the ECE department. It will include a brief presentation of the student s research to date, followed by an oral examination based on two courses of the student s choosing. Professors will have a wide latitude to choose the questions from among these general areas, in order to evaluate the student s thought process and mastery of the field. Passing the exam is contingent upon a consensus decision of the exam committee, which includes three faculty members of the student s choosing, accepted by the student s advisor. Any changes to the ECE department policy on preliminary exams will flow down to this program. 5. Qualifying examinations oral. The qualifying exam will be consistent with the rest of the ECE department. It will include a summary of the student s research results to date, and their research plans for finishing their degree. Passing the exam is contingent upon a consensus decision of the exam committee, which will consist of five members, including two from outside the department, selected by the student and approved by the student s advisor. Any changes to the ECE department policy on qualifying exams will flow down to this program. 6. Thesis and/or dissertation. The requirements for the thesis will be consistent with the rest of the ECE department. The thesis will be a written exposition of the student s research work. It will be reviewed by the members of the final defense committee, who are typically the same as the members of the qualifying exam committee. Any changes to the ECE department policy on thesis requirements will flow down to this program. 7. Final examination. The final defense exam will be consistent with the rest of the ECE department. It will include an oral presentation of the student s research. Passing the exam is contingent upon a consensus decision of the exam committee, which will consist of five members, including two from outside the department, selected by the student and approved by the student s advisor. They are typically the same as the members of the qualifying exam committee Any changes to the ECE department policy on final defenses will flow down to this program. 8. Explanation of special requirements over and above Graduate Division minimum requirements. None. 9. Relationship of master s and doctor s programs (if applicable). There is no particular relationship between the M.S. and Ph.D. programs, other than sharing the same classes. Students in the M.S. program may join the Ph.D. program if they find a willing advisor, as with any other area within the ECE department.

10. Special preparation for careers in teaching. There is no particular preparation for careers in teaching. Those students who wish to pursue an academic career may choose to be TAs for various ECE classes to gain practical experience in the classroom. 11. Sample program. Below we are providing a comparison of the course requirements in Machine Learning and Data Science, and the course requirements in Intelligent Systems, Robotics, and Control. While these two majors have different areas of focus, these are the two programs of study in Electrical and Computer Engineering that have the most overlap. In addition to the course requirements listed below in these sample programs, all M.S. students in the Electrical and Computer Engineering Department must pass a written comprehensive exam or defend a thesis prior to graduation, and all Ph.D. students must pass the preliminary exam by the end of the second year, pass the university qualifying exam by the end of the fourth year, and pass the final defense of the dissertation prior to graduation. Machine Learning and Data Science 4 Core Courses: Intelligent Systems, Robotics, and Control 4 Core Courses: ECE 143 Programming for Data Analysis ECE 269 Linear Algebra & Applications ECE 269 Linear Algebra & Applications ECE 271A Statistical Learning I ECE 271A Statistical Learning I ECE 272A Stochasitc Processes in Dynamic Systems I ECE 225A Probability & Statistics for Data Science ECE 276A Sensing & Estimation in Robotics 4 Additional Courses (at least 1 in each area) from the following: 3 Additional Courses from the following: Analytics ECE 250 Random Processes ECE 271B Statistical Learning II ECE 252A-B Speech Compression; Speech Recognition ECE 273 Convex Optimization & Applications ECE 253 Fundamentals of Digital Image Processing ECE 275A Parameter Estimation ECE 271B Statistical Learning II Computation ECE 2xx ECE 2xx ECE 2xx Applications Optimization and Acceleration of Deep Learning Parallel Processing in Data Science Scalable Learning ECE 272B ECE 273 ECE 275A-B ECE 276B-C Stochasitc Processes in Dynamic Systems II Convex Optimization & Applications Parameter Estimation I & II Planning & Learning in Robotics; Advances in Robot Manipulation ECE 276A ECE 276B Sensing & Estimation in Robotics Planning & Learning in Robotics ECE 285 Special Topics in Signal and Image Processing/Robotics and Control Systems 4 Additional Technical Elective Courses CSE 250A CSE 252A CSE 253 MAE 247 MAE 280A MAE 281A Artificial Intelligence: Search and Reasoning Computer Vision I Neural Networks for Pattern Recognition Cooperative Control of Multi-Agent Systems Linear Systems Theory Nonlinear Systems 5 Additional Technical Elective Courses

12. Normative time from matriculation to degree. The normal time to degree is expected to be 2 years for the M.S., and 6 years for the Ph.D. Degree progress limits and cut-off times will be consistent with the rest of the department, and are subject to ECE and UCSD rules. Section 3. Projected Need 1. Student demand for the program. Machine learning and data science are among the most popular topics among current students. This is evidenced by the number of students taking our graduate machine-learning classes, which often exceeds 100 compared to typically less than 50 in other classes. 2. Opportunities for placement of graduates. According to several surveys, there is a very favorable market for data-science graduates. For example, in 2015, the international data corporation anticipated a shortage of 180,000 data scientists in 2018, the Sloan Review wrote that 40% of companies struggle to recruit and retain talent in data science, and Glassdoor reported that starting salaries for data scientists are significantly higher than those in related disciplines. 3. Importance to the discipline. Machine learning and data science are central to many facets of electrical and computer engineering. There is strong demand for specialists trained in these areas and we currently have significant strength in these areas. However, we do not have a specific major to which student can apply. The establishment of a major code in Machine Learning and Data Science will increase visibility of our strengths, and lead to more and higher quality applicants, further enhancing UCSD s strong reputation in engineering. 4. Ways in which the program will meet the needs of society. There is currently a shortage of individuals with strong data science skills, and those who possess these skills are in high demand. We are addressing the machine learning and data science talent shortage by creating this program which will enhance our ability to provide high quality job candidates to a wide range of organizations including large high-tech companies and government agencies. 5. Relationship of the program to research and/or professional interests of the faculty. A large number of faculty members in the Electrical and Computer Engineering Department already have research groups that are focused on aspects of Machine Learning and Data Science: Nikolay Atanasov: Theoretical and computational aspects of control theory, optimization, and machine learning and their application to sensing, estimation, planning, and control in robotics. Particular focus on autonomous information collection (metric, semantic, topological) using teams of ground and aerial robots in applications such as mapping, security and surveillance, and environmental monitoring. Sujit Dey: Development and use of data science algorithms and architectures in diverse applications such as connected health, solar energy generation and distribution, online video classification, augmented and virtual reality, and massive MIMO systems. Research includes both deep learning techniques suitable for the applications, as well as new hybrid cloud and edge architectures to ensure scalable and real-time solutions with high efficiency in computation and data communication.

Massimo Franceschetti: Network science, network representations from data, data mining and analysis, social networks, network structure and organization, contact processes on networks, phase transitions, filtering, prediction, estimation, causality, classification. Vikash Gilja: Applications of data science to basic neuroscience, clinical measurements of behavior and physiology, and neural engineering. Statistical signal processing, generative models, and neural network based approaches applied to high-dimensional time series measurements for data exploration, hypothesis generation, clinical diagnostics, and real-time neural decoding for prosthetic applications. Todd Hylton: Physically motivated learning algorithms for embodied, physical systems - robots, for example. Leaming the physics of complex, changing real-world environments through interaction and experience with it. Massively recurrent neural networks that learn the dynamics of their environment by training to predict inputs. Creating connections between machine learning techniques and basic concepts in thermodynamics. Implementation of the above concepts in distributed, asynchronous computing systems. Tara Javidi: Information acquisition and interactive data analytics. Active machine learning. Machine learning for (wireless) network management. Controlled sensing, filtering, estimation, and tracking. Sequential and hierarchical image analysis. Real-time active tracking with applications to unmanned drones and vehicles. Andrew B. Kahng: Applications of data science to integrated-circuit design and performance analyses. Recent projects include estimation and prediction of post-layout physical and electrical parameters, reduction of model-hardware miscorrelation and design margins, and modeling and characterization of complex IC design tools and design flows. Broad interests across heuristic global optimization, clustering, classification, modeling and estimation. Young-Han Kim: Information theory and data science. Universal probability and its applications to data science such as compression, portfolio selection, prediction, filtering, estimation, denoising, classification, and recommendation. Causality. Network information flow. DNA/RNA classification. Farinaz Koushanfar: Digital signal processing, real-time and low power data mining/analytics, deep neural networks/deep learning, stream sketching, online probability density function (PDF) estimation, real-time and low overhead time-series analysis, real-time causal modeling/mcmc, software and/or hardware-based acceleration of data analytics, GPU and FPGA acceleration, model assurance, privacy-preserving data-analytics, applications of learning in security and computer vision. Ken Kreutz-Delgado: Theoretical and computational aspects at the interface between statistical signal processing, machine learning, and neural networking processing. Modeling of stochastic dynamical systems, time-series and stochastic-sampling algorithms. Applications to brain-imaging, robotics, the design of stochastic neural networks, and hardware-based acceleration of stochastic neural networks. Gert Lanckriet: Machine learning, optimization, deep learning, information retrieval, multimedia, recommender systems, wearable sensors, context awareness, computer audition. Bill Lin: Computational aspects of data science, including efficient algorithms and system architectures for processing and learning from big data. Applications include data analytics problems in various domains. Siavash Mirarab: Application of data sciences to biology, and particularly, understanding evolution. Methods scalable to hundreds or thousands of genomes. Projects (all collaborative) include: inference of a tree-of-life of bacteria from tens of thousands of genomes and its applications to microbiome studies and inference of HIV transmission networks from genomic data. Truong Nguyen: Data science applications to image/video processing and analysis. Recent projects include machine learning methods for object detection, semantic segmentation, instance segmentation, denoising, super-resolution, sign language (gesture) recognition and 3D reconstruction.

Alon Orlitsky: Theoretical foundations of Data Science. Modeling, estimation, prediction, classification and learning of distributions and processes. Practical algorithms and fundamental limits of learning from data. Applications to biology, compression, communication, and finance. Piya Pal: Statistical information processing of high dimensional signals. Design and analysis of new energy-efficient sensing and sketching techniques for big data, and development of computationally efficient robust algorithms (convex and non convex) with provable performance guarantees. Specific applications include sensor array signal processing for localization and tracking, data sketching and high dimensional time series analysis, tensor decomposition, super-resolution microscopy and ill-posed inverse problems in biomedical imaging. Bhaskar Rao: Digital signal processing, single and multi-channel time series analysis, compressed sensing and sparse signal recovery, Bayesian methods, microphone arrays and multi-antenna wireless communications. Paul Siegel: Algebraic, probabilistic, and constrained coding for data transmission and storage. Information theory of data storage systems. Recent projects include: Algebraic coding techniques for computer memories and distributed data networks. Graphical models, probabilistic inference algorithms, and optimization techniques applied to decoding of graph-based error-correcting codes. Matrix analysis and Markov chain theory applied to graphical structures, with applications to coding for constrained systems. Information-theoretic models of data storage devices and systems, analysis of theoretical storage limits. Alexander Vardy: Coding for reliable and efficient data transmission and storage, with emphasis on advanced coding techniques for distributed data retrieval systems. Current research focuses on regenerating codes for efficient repair of failed nodes in distributed data-storage networks, availability codes for simultaneous access to "hot" data by multiple users, and local recovery coding for data-management systems (e.g. Apache Hadoop). Other recent projects include private information retrieval from distributed databases and coding for emerging memory/storage technologies. Nuno Vasconcelos: Computer vision, machine learning, and multimedia. Current research emphasizes deep learning for object recognition and detection, action understanding, scene classification, image semantics, image retrieval, image hashing, models of saliency and attention, smart cars, and various other problems in computer vision. Work on the foundations of deep learning included the design of loss-functions, regularization, network architecture, transfer learning, low-precision networks, etc. Video analysis, including action recognition, american sign language translation, video surveillance, crowd counting, etc. Other work includes generative modeling, Bayesian inference, linear dynamic systems, the foundations of boosting, cost-sensitive learning, and biologically plausible architectures for computer vision. Michael Yip: Application of data sciences to robotics and medical imaging. Reinforcement learning for teaching expert skills to surgical robots and for robots to learn to manipulate objects and themselves. Image-based, 3D spatial reasoning, localization, and 3D scene reconstruction using neural network approaches. 6. Program Differentiation. A number of universities in California offer classes related to Machine Learning and Data Science, but there are very few specific graduate programs in this area. Creating a specific program in Machine Learning and Data Science will identify this as an area of strength for UC San Diego and the Jacobs School of Engineering. We predict it will allow us to attract a larger and higher quality body of applicants in this area to our Electrical and Computer Engineering department. Section 4. Faculty The following faculty members are currently conducting research and/or teaching courses in this area:

Nikolay Atanasov Sujit Dey Massimo Franceschetti Vikash Gilja Todd Hylton Tara Javidi Andrew Kahng Young-Han Kim Farinaz Koushanfar Ken Kreutz-Delgado Gert Lanckriet Bill Lin Siavash Mirarab Truong Nguyen Alon Orlitsky Piya Pal Bhaskar Rao Paul Siegel Alexander Vardy Nuno Vasconcelos Michael Yip CVs for participating faculty are included with this proposal. Section 5. Courses The Electrical and Computer Engineering Department has been offering many of the courses we are proposing for the Machine Learning and Data Science major for many years, and others have been added to our course offerings in the last two to three years. They will continue to be staffed the same way that they have been for the past several years, with typical instructors listed after the course titles (see Section 2.3.c., and below). Thus, there is no effect on staffing levels. Additional faculty members may be hired as necessary as the program expands in time, in line with the overall growth of the department. The core courses are: ECE 143 (currently ECE 188, Special Topic) - Programming for Data Analysis (Instructor varies) This is a hands- on course designed to teach students Python and its usage in Data Science applications. Topics include: understanding Python object -oriented and functional programming styles, learning key scientific computing packages, applying key Python data structures and algorithms effectively, enhancing productivity with Python development workflows, and developing deployable codes using modern package management and source control. ECE 269 - Linear Algebra and Application (Piya Pal) This course will build mathematical foundations of linear algebraic techniques and justify their use in signal processing, communication, and machine learning. Topics include geometry of vector and Hilbert spaces, orthogonal projection, systems of linear equations and role of sparsity, eigenanalysis, Hermitian matrices and variational characterization, positive semidefinite matrices, singular value decomposition, and principal component analysis. Prerequisites: graduate standing. ECE 271A - Statistical Learning I (Nuno Vasconcelos) Bayesian decision theory; parameter estimation; maximum likelihood; the bias-variance trade-off; Bayesian estimation; the predictive distribution; conjugate and noninformative priors; dimensionality and dimensionality reduction; principal component analysis; Fisher s linear discriminant analysis; density estimation; parametric vs.

kernel-based methods; expectation-maximization; applications. (Recommended prerequisites: ECE 109.) Prerequisites: graduate standing. ECE 225A (currently ECE 289, Special Topic) - Probability and Statistics for Data Science (Alon Orlitsky) The course reinforces students intuitive, theoretical, and computational understanding of probability and statistics and builds on these foundations to introduce more advanced concepts useful in both data-science research and practice. The following topics will be covered. Basics: counting, probability, random variables, common distributions, distribution properties. Inequalities: Markov, Chebyshev, Chernoff, Jensen. Convergence: modes of convergence, laws of large numbers, central limit theorem. Estimation: sampling, mean squared error, confidence intervals, regression. Hypothesis testing: error types, significance tests, p values. Python programs, examples, and visualizations will be used throughout the course. Prerequisites: graduate standing. Section 6. Resource Requirements Estimated for the first 5 years the additional cost of the program, by year, for each of the following categories: 1. FTE faculty none 2. Library acquisition none 3. Computing costs none 4. Equipment none 5. Space and other capital facilities none 6. Other operating costs none No new resources will be required for this program. Professors who are involved will continue to teach the same classes. Any expansion of the program will be part of the overall ECE department expansion and hiring plan. The involved professors already have established laboratories, space, computing resources, etc. Section 7. Graduate Student Support Graduate students will be supported in the same way as they are currently supported in the overall ECE department. Our MS students are typically offered admission without funding. For PhD students, support typically involves a fellowship for the first year, followed by graduate student research positions paid by grants or contracts for the remainder of their studies. The fellowships will come from the overall pool for the department. Involved professors already receive fellowship allocations from other groups, and those would be reduced accordingly so that each professor gets, on average, the same number of fellowships regardless of which group they are involved with, or if they are involved with multiple groups. Graduate student research positions for the following years will be paid by the grants and contracts of the professors who are involved with the Machine Learning and Data Science program. Professors who are already doing work in this area are already supporting their students on existing grants and contracts, and nothing will change with the creation of the Machine Learning and Data Science program. Those students are already taking the Machine Learning and Data Science courses that are already available to them, so this program will not change anything with regards to student support. It will only involve re-allocation or re-naming of some support structures to be associated with this program, but will not change the overall average support profile for any of the professors, nor of those associated with other groups. Teaching assistantships are generally allocated to the professors who are teaching the corresponding courses, and they choose graduate students based on class needs, and the skills and experience of the applicants. This will not change with the creation of the Machine Learning and Data Science program. Average per student support from all sources will remain exactly as it is, consistent with ECE department averages. Section 8. Governance

The Department or Group that will administer the program is the Electrical and Computer Engineering Department. This program will be governed in the same way as all other programs within the department. This proposal is only a mechanism to allow students to apply to this topic, and to officially take their preliminary exams in this topic without requiring an exception.

Appendices Appendix A: Letters of Support from the Dean of JSOE and the UCSD Data Science Institute, and feedback from Chairs of Programs Offering a Comparable Degree at Other UC Campuses 1) List of Chairs and Program Directors to whom the proposal was sent for comments 2) Sample text of letter sent to Chairs 3) Response from Chairs Appendix B: CVs of involved faculty Nikolay Atanasov Sujit Dey Massimo Franceschetti Vikash Gilja Todd Hylton Tara Javidi Andrew Kahng Young-Han Kim Farinaz Koushanfar Ken Kreutz-Delgado Gert Lanckriet Bill Lin Siavash Mirarab Truong Nguyen Alon Orlitsky Piya Pal Bhaskar Rao Paul Siegel Alexander Vardy Nuno Vasconcelos Michael Yip Appendix C: Revised Catalog Copy

Appendix A: Letters of Support from the Dean of JSOE and the UCSD Data Science Institute, and feedback from Chairs of Programs Offering a Comparable Degree at Other UC Campuses 1. This Machine Learning and Data Science program proposal was sent to: -Al Pisano, Dean of the UCSD Jacobs School of Engineering -Rajesh Gupta and Jeffrey Elman of the UCSD Halicioglu Institute for Data Science -UCSD s CSE Department -UCLA s ECE Department -UC Berkeley s School of Information 2. Sample text of letter sent to solicit feedback: Dear Professor xxxx, At UCSD we are in the process of proposing a new graduate program in Machine Learning and Data Science leading to M.S. and Ph.D. degrees within the Electrical and Computer Engineering Department of the Jacobs School of Engineering. In accordance with the review policy established by the system-wide Coordinating Committee of Graduate Affairs (CCGA), I am providing you, as the Chair of an existing comparable program, with a copy of the current draft of our proposal. We would be very grateful for any feedback you may wish to offer us, so that the proposal may be made as strong as possible before submission. As background, please understand that the format and contents of the proposal follow the required outline found in the CCGA Handbook, and that internal and external reviewers will later be asked to address the following four points when examining our final submission: Quality and academic rigor of the program Adequacy of the size and expertise of faculty to administer the program Adequacy of the facilities and budgets Applicant pool and placement prospects for the graduates If you wish to provide feedback, we would like to receive it within three weeks of the date of this letter, since we expect to submit the proposal for campus review at that time. 3. Responses from Chairs/Deans/Department heads We received responses from Dean Pisano, the Halicioglu Institute for Data Science, the ECE Department at UCLA and the School of Information at UC Berkeley. The responses are strong and supportive of our Machine Learning and Data Science program proposal. Only two suggestions were made: the first is to consider increasing the number of computational classes within the scope of the required course work, and the second is to formally address the ethical and privacy issues that are raised by the growing use of machine learning and data. Once we start teaching these courses we will evaluate the suggestion offered about computing, and while we anticipate that ethics and privacy issues will be addressed in many of the courses offered we will also evaluate how we can best highlight these issues in the curriculum. The responses from the heads of other schools and departments are included below.

November 27, 2017 Truong Nguyen, Ph.D. Professor and Chair Electrical and Computer Engineering Dept. University of California, San Diego 9500 Gilman Dr. La Jolla, CA 92093-0407 Dear Truong, Thank you for your leadership in pulling together a proposal for addition of new graduate major field of study in Machine Learning and Data Science to the MS and PhD degrees available within the Electrical and Computer Engineering Department in the Jacobs School of Engineering. We have reviewed the proposal and fully appreciate the intent of the new graduate program option to prepare students for an interdisciplinary career in the emerging area. We note program focus on foundational subjects in statistical learning and optimization and consider it to be a particular strength of the program while at the same time providing access to a large and diverse group of application areas where machine learning can be applied. We also see a potential benefit to students from other departments who wish to enhance their graduate studies by taking the comprehensive set of classes offered in this program, on the theory, implementation and application of Machine Learning and Data Science. Overall the program is an attractive option among a dozen or so majors in the ECE department and will complement current area of Intelligent Systems, Robotics and Control. We note that the new major options requires existing total credit requirements to ensure a strong graduate program. We look forward to partnering with the ECE Department on faculty recruitment, collaborative research initiatives, and summer research internship initiatives. We are pleased to offer our support for the success of the program, Sincerely yours, Rajesh K. Gupta Professor of Computer Science & Eng. Jeffrey L. Elman Distinguished Professor of Cognitive Science

From: Greg Pottie <gpbutterflies@gmail.com> Subject: Re: Request for comments on our Department's effort to create a graduate degree in machine learning and data science. Date: December 4, 2017 at 12:52:44 PM PST To: Alon Orlitsky <aorlitsky@eng.ucsd.edu> Dear Professor Orlitsky I have reviewed the proposal for the proposed graduate program in Machine Learning and Data Science in the UCSD ECE Department. In my opinion, it will be a rigorous and popular major, of considerable professional value to the students. It builds on a number of existing graduate courses in the Department, and reflects the research interests of a significant fraction of the faculty as a whole. As noted in the proposal courses already offered in this area have enrollment considerably above the Department average. The only resource issue to consider with this proposal is that the enhanced visibility will attract even more students, and so the Department will need to prepare for large enrollments in the core graduate courses of the major (e.g., by providing adequate TA or Special Reader resources). I would expect that the increased visibility will also attract more students from other majors at UCSD; if there are lecture hall size limitations, some prioritization for enrollment of ECE students might become necessary via the mechanism of wait lists. In other words, the only risk is that the program will be too successful. Regards Greg Pottie Professor and Chair, UCLA ECE Department On Wed, Nov 22, 2017 at 7:26 AM, Alon Orlitsky <aorlitsky@eng.ucsd.edu> wrote: Dear Professors Pottie and Roychowdhury, Please forgive this somewhat formal email. It is part of our department s official effort to create a graduate data-science focus. Your help with some feedback, which can be brief, on the proposal would be greatly appreciated. Following is the official mail I was asked to send. Thank you very much for your help! - Alon At UCSD we are in the process of proposing a new graduate program in Machine Learning and Data Science leading to M.S. and Ph.D. degrees within the Electrical and Computer Engineering Department of the Jacobs School of Engineering. In accordance with the review policy established by the system-wide Coordinating Committee of Graduate Affairs (CCGA), I am providing you, as the Chair of an existing comparable program, with a copy of the current draft of our proposal. We would be very grateful for any feedback you may wish to offer us, so that the proposal may be made as strong as possible before submission. As background, please understand that the format and contents of the proposal follow the required outline found in the CCGA Handbook, and that internal and external reviewers will later be asked to address the following four points when examining our final submission: Quality and academic rigor of the program Adequacy of the size and expertise of faculty to administer the program Adequacy of the facilities and budgets Applicant pool and placement prospects for the graduates If you wish to provide feedback, we would like to receive it within three weeks of the date of this letter, since we expect to submit the proposal for campus review at that time. Thank you very much, Alon Orlitsky

Comments on the Proposed Machine Learning and Data Science Graduate Program to be added to the M.S. and Ph.D. degrees available within the Electrical and Computer Engineering Department, UCSD Effective Fall 2018 By Vwani Roychowdhury UCLA Quality and academic rigor of the program This is a very timely program, and the proposed courses and requirements (for both MS and Ph.D. studies) have been carefully thought out and crafted so that they strike a judicious balance between many of the theoretical foundations of machine learning, and the pressing need for integrating data science and machine learning into a wide array of applications and products. I believe that the Aims and objectives of the program of the report correctly points out that the enabling driving forces behind the current era of information revolution are the availability of large-scale and diverse data sets, and the ubiquitous access to computing resources to store and process such data in a timely fashion. A lot of the conceptual and theoretical foundations that have already been developed, especially by those working in Information theory and Statistical Signal processing areas, are now practicable for the first time: There is enough data to model complex systems, and there is sufficient computing power to implement the various algorithms and data models that have been around for a while now. Deep Learning is an important data point in this regard: It implements ideas and algorithms known for several decades, but enabled only now by access to large-scale training data and matching computing power. I am heartened to see that the proposed effort acknowledges these core realities upfront and then proceeds to design a program that combines core strengths of the department in statistical information processing, machine learning and computing to design a first rate program that will prepare students for effective career in data science and applications. My only suggestion is that the program could emphasize the computing aspects of data science a bit more in the required course work. Currently I see that the following 100- level course, ECE 143: Programming for Data Analysis. is a required core course in the Data Science and Machine Learning program; in addition, the students must take at least another course from the Computation area, which has three 200-level courses (ECE 2xx) that I assume are yet to be developed. Perhaps, having a sequence of two core courses on the computing aspects of Big Data would be more appropriate. Proficiency and fluency in

the computing aspects of data processing, and being able to program the ideas and algorithms that the students learn in theory courses, is a defining characteristics of the data science movement that we are witnessing now. So my suggestion would be to perhaps strengthen this computing aspect more, which is already strong in the current proposal. Adequacy of the size and expertise of faculty to administer the program UCSD ECE department has a world-class faculty and as I already mentioned, modern data science and machine learning practices borrow heavily from the theory and concepts developed in the traditional Electrical Engineering and Computer Science disciplines. Thus ECE is particularly suited to be offering such a program. Adequacy of the facilities and budgets ECE department at UCSD has a long history of providing world-class education and I have full confidence in its ability to provide adequate resources for a successful implementation of the program. Applicant pool and placement prospects for the graduates There is a strong student demand for a program like this. In courses that we teach at UCLA related to Data Science and ML, typical graduate student enrollments exceed 150 routinely per class offering. Moreover, both in industry and academy there is a strong need for hiring in this field. All forecasts point to a strong demand for graduates of such programs for many years to come. Concluding Remarks: This is a timely and a very well designed program that will benefit both UCSD and the student body. It will also greatly benefit the larger eco-system comprising of academy, research and industry and contribute to the economy and the society at large. I strongly support this program.

UNIVERSITY OF CALIFORNIA, BERKELEY BERKELEY DAVIS IRVINE LOS ANGELES RIVERSIDE SAN DIEGO SAN FRANCISCO SANTA BARBARA SANTA CRUZ December 5, 2017 OFFICE OF THE DEAN SCHOOL OF INFORMATION BERKELEY, CALIFORNIA 94720-4600 Dear Professor Orlitsky: I am happy to provide my input on the proposed new program in Machine Learning and Data Science leading to MS and PhD degrees in the Electrical and Computer Engineering Department (ECED) of the UCSD Jacobs School of Engineering This is a timely proposal. It is important for the University of California to play a leadership role in data science, a field that has strong roots in this state. Machine learning and the related statistical and computational tools are transforming not only scholarly research but also many aspects of the economy and society. Data science education is an investment in our future. The UC Berkeley School of Information launched a self-supporting Master s of Information and Data Science (MIDS) degree in 2014 to educate professionals seeking to be data science leaders in their organizations. The proposal under review, by contrast, adds a new field of study to the existing state-supported ECED graduate programs, allowing Masters and PhD students to specialize in Machine Learning and Data Science. Quality and Academic Rigor This is a very strong proposal. The ECED at the Jacobs School of Engineering is home to a substantial core of faculty members working in probability, statistical machine learning, and related analytical and computational tools/techniques, as well as key application areas (from robotics and bio signal processing, to network data and physical and embedded systems.) The program is well designed and technically rigorous. The 4 core courses provide a strong foundation in the mathematics, probability, statistics, and programming skills that all students will need for more advanced graduate work in the field. The 4 required courses in analytics, computation, and applications will provide students with in-depth exposure to current research in machine learning and data science tools and applications. Ideally some of the technical electives will also be in related fields, further deepening their expertise. While this is a solid program of technical education, I would be remiss in not asking about how the curriculum exposes students to the ethical and privacy issues that are raised by the growing use of machine learning and data at scale. I suspect that the Jacobs School offers courses on the social and human aspects of technology but they may need updating. The collection and use of data to address human, societal, and scientific problems raises important issues, ranging from potential biases in the data collection and classification process to questions about ownership of personal data and privacy in a world of ubiquitous sensor networks. Graduate students who will work in this field should be exposed to these issues.

Adequacy of the size and expertise of faculty to administer the program The proposal lists 21 ECED faculty members who will teach and administer the program all of whom are doing research that is relevant to machine learning and data science. This represents a healthy mix of Assistant Professors, Professors, and Distinguished Professors. Most (if not all) of the courses for the program are already being taught, and there appears to be more than sufficient personnel to staff all of the core courses as well as the electives. In short, the scale and expertise of the faculty are more than adequate to mount a strong program in the area. This program is likely to increase the visibility of ECED and, over time, grow enrollment because of the robust demand for machine learning and data science education. Over time the department may need additional resources. That should, however, easily handled within the normal unit and campus recruitment processes. Adequacy of the facilities and budgets The program does not require any additional facilities or other resources; rather it involves redeploying existing faculty and classes to this new program. The professors who are involved have ongoing research contracts and grants that they will use to support PhD students in the field. They already have laboratories, space, and computing resources. As noted above, if demand is substantial additional facilities and budgets may be needed, but not immediately. Applicant pool and prospects for graduates There is far more demand for programs in the field of machine learning and data science than there is supply. MIDS has grown from an initial cohort of 28 students in 2014 to close to almost 500 enrolled today and we accept only 25-30% of total applicants. I have no doubt that a strong program with a high quality faculty, like that in ECED, will be attractive to existing students, and has the potential to increase applications to the department s graduate programs. There are now many studies documenting the shortages of graduates with data science skills. Graduates of our MIDS program increase their salaries by over 70%. Our experience shows that there is strong demand for graduates with these skills from every sector of society: small firms, large firms, consumer-oriented and enterprise-oriented businesses, a wide range of industries, government and non-profit organizations, as well as corporate research labs and universities. We have had no problem placing grads in attractive and well-paid positions, and I have little reason to believe that the UCSD program would be different. Thanks for the opportunity to comment on this program. Feel free to contact me with any additional questions. Best regards, AnnaLee Saxenian Dean & Professor 2

Contact Information Nikolay A. Atanasov Atkinson Hall #6127 Contextual Robotics Institute Phone: (858) 534-4105 University of California, San Diego E-mail: natanasov@ucsd.edu 9500 Gilman Drive MC 0436 Web: natanaso.github.io La Jolla, CA 92093 Academic Appointments Assistant Professor Department of Electrical and Computer Engineering University of California, San Diego, CA Jul. 2017 - Present Postdoctoral Researcher Sep. 2015 - Jun. 2017 Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania, Philadelphia, PA Mentors: Vijay Kumar and George J. Pappas Education Ph.D., Electrical and Systems Engineering Dec. 2015 University of Pennsylvania, Philadelphia, PA Thesis: Active Information Acquisition with Mobile Robots Advisors: George J. Pappas and Kostas Daniilidis Joseph and Rosaline Wolf Best Dissertation Award M.S., Electrical Engineering Aug. 2012 University of Pennsylvania, Philadelphia, PA B.S., Engineering May 2008 Trinity College, Hartford, CT Research Interests Probabilistic environment representations that unify geometry, semantics, and physics; visual-inertial odometry; simultaneous localization and mapping; dense metric and semantic mapping; Autonomous information collection using ground and aerial robots for localization and mapping, environmental monitoring, and security and surveillance; optimal and model predictive control with information theoretic objectives; reinforcement learning; exploration; motion planning; active perception; distributed inference and planning; Relevant fields: robotics, machine learning, control theory, optimization, computer vision Teaching Experience Lecturer, Sensing and Estimation in Robotics, University of California, San Diego Fall 2017 Lecturer, Learning in Robotics, University of Pennsylvania Spring 2017 Guest Lecturer, Nonliner Systems and Control, University of Pennsylvania Fall 2015 Guest Lecturer, Linear Systems Theory, University of Pennsylvania Fall 2011 Teaching Assistant, Linear Systems Theory, University of Pennsylvania Fall 2010, Fall 2011 Teaching Assistant, Calculus I, Trinity College, Hartford, CT Fall 2006, Fall 2007 1

Nikolay A. Atanasov Curriculum Vitae Teaching Assistant, Calculus II, Trinity College, Hartford, CT Spring 2006, Spring 2007 Teaching Assistant, Intro to Computer Science, Trinity College, Hartford, CT Fall 2005 Honors & Awards Best Conference Paper Award 2017 IEEE International Conference on Robotics and Automation (ICRA 17) for the paper Probabilistic Data Association for Semantic SLAM Joseph and Rosaline Wolf Best Ph.D. Dissertation Award 2015 Awarded by the School of Engineering and Applied Science, University of Pennsylvania Phi Beta Kappa Society 2008 - Present President s Fellow, Trinity College, Hartford, CT 2007-2008 Awarded for outstanding work in the Engineering major Thomas Holland Scholarship, Trinity College, Hartford, CT 2006-2008 Awarded for attaining the highest rank in the junior and senior classes Phi Gamma Delta Teaching Fellowship, Trinity College, Hartford, CT 2006, 2007 Awarded for aiding the Department of Mathematics in its instructional endeavors Journal Articles J4. N. Atanasov, M. Zhu, K. Daniilidis, and G. Pappas, Localization from semantic observations via the matrix permanent, The International Journal of Robotics Research (IJRR), vol. 35, pp. 73 99, 2015 J3. N. Atanasov, J. Le Ny, and G. Pappas, Distributed algorithms for stochastic source seeking with mobile robot networks, ASME Journal of Dynamic Systems, Measurement, and Control (JDSMC), vol. 137, no. 3, pp. 031 011 031 011 9, 2015 J2. N. Atanasov, B. Sankaran, J. Le Ny, G. Pappas, and K. Daniilidis, Nonmyopic view planning for active object classification and pose estimation, IEEE Trans. on Robotics (TRO), vol. 30, no. 5, pp. 1078 1090, 2014 J1. J. Ning and N. Atanasov, Delineation of Systolic and Diastolic Heart Murmurs via Wavelet Transform and Autoregressive Modeling, International Journal of Bioelectromagnetism, vol. 12, no. 3, 2010 Conference Proceedings C22. S. Liu, N. Atanasov, K. Mohta, and V. Kumar, Search-based Motion Planning for Quadrotors using Linear Quadratic Minimum Time Control, in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2017 C21. M. Zhang, N. Atanasov, and K. Daniilidis, Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search, in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2017 C20. A. Zhu, N. Atanasov, and K. Daniilidis, Event-based Visual Inertial Odometry, in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2017 C19. A. Zhu, N. Atanasov, and K. Daniilidis, Event-based Feature Tracking with Probabilistic Data Association, in IEEE Int. Conf. on Robotics and Automation (ICRA), 2017 C18. S. Bowman, N. Atanasov, K. Daniilidis, and G. Pappas, Probabilistic Data Association for Semantic SLAM, in IEEE Int. Conf. on Robotics and Automation (ICRA), 2017 (Best Paper Award) C17. V. Tzoumas, N. Atanasov, A. Jadbabaie, and G. Pappas, Scheduling Nonlinear Sensors for Stochastic Process Estimation, in American Control Conference (ACC), 2017 C16. C. Di Franco, A. Prorok, N. Atanasov, B. Kempke, P. Dutta, V. Kumar, and G. Pappas, Calibration- Free Network Localization using Non-Line-of-Sight Ultra-Wideband Measurements, in ACM/IEEE Int. Conf. on Information Processing in Sensor Networks (IPSN), 2017 C15. J. Fu, N. Atanasov, U. Topcu, and G. Pappas, Optimal Temporal Logic Planning in Probabilistic Semantic Maps, in IEEE Int. Conf. on Robotics and Automation (ICRA), 2016 2

Nikolay A. Atanasov Curriculum Vitae C14. R. Ivanov, N. Atanasov, J. Weimer, M. Pajic, A. Simpao, M. Rehman, G. Pappas, and I. Lee, Estimation of Blood Oxygen Content Using Context-Aware Filtering, in ACM/IEEE Int. Conf. on Cyber-Physical Systems (ICCPS), 2016 C13. R. Ivanov, N. Atanasov, M. Pajic, G. Pappas, and I. Lee, Robust Estimation Using Context-Aware Filtering, in Allerton Conference on Communication, Control, and Computing, 2015 C12. N. Atanasov, J. Le Ny, K. Daniilidis, and G. Pappas, Decentralized active information acquisition: Theory and application to multi-robot slam, in IEEE Int. Conf. on Robotics and Automation (ICRA), 2015, pp. 4775 4782 C11. N. Atanasov, R. Tron, V. Preciado, and G. Pappas, Joint estimation and localization in sensor networks, in IEEE Conf. on Decision and Control (CDC), 2014, pp. 6875 6882 C10. M. Zhu, N. Atanasov, G. Pappas, and K. Daniilidis, Active deformable part models inference, in European Conf. on Computer Vision (ECCV), vol. 8695, 2014, pp. 281 296 C9. N. Atanasov, M. Zhu, K. Daniilidis, and G. Pappas, Semantic Localization via the Matrix Permanent, in Robotics: Science and Systems (RSS), 2014 C8. N. Atanasov, J. Le Ny, K. Daniilidis, and G. Pappas, Information acquisition with sensing robots: Algorithms and error bounds, in IEEE Int. Conf. on Robotics and Automation (ICRA), 2014, pp. 6447 6454 C7. N. Atanasov, B. Sankaran, J. Le Ny, T. Koletschka, G. Pappas, and K. Daniilidis, Hypothesis testing framework for active object detection, in IEEE Int. Conf. on Robotics and Automation (ICRA), 2013, pp. 4216 4222 C6. N. Atanasov, J. Le Ny, N. Michael, and G. Pappas, Stochastic source seeking in complex environments, in IEEE Int. Conf. on Robotics and Automation (ICRA), 2012, pp. 3013 3018 C5. T. Ning, J. Ning, N. Atanasov, and K. Hsieh, A Fast Heart Sounds Detection and Heart Murmur Classification Algorithm, in IEEE Int. Conf. on Signal Processing (ICSP), vol. 3, 2012 C4. J. Ning, N. Atanasov, and T. Ning, Quantitative Analysis of Heart Sounds and Systolic Heart Murmurs Using Wavelet Transform and AR Modeling, in IEEE Int. Conf. on Engineering in Medicine and Biology (EMBC), 2009 C3. N. Atanasov and T. Ning, Isolation of Systolic Heart Murmurs Using Wavelet Transform and Energy Index, in IEEE Congress on Image and Signal Processing (CISP), 2008 C2. N. Atanasov and T. Ning, Quantitative Delineation of Heart Murmurs Using Features Derived from Autoregressive Modeling, in IEEE Northeast Bioengineering Conference (NEBC), 2007 C1. T. Ning, S. Bhandari, and N. Atanasov, Restoration of Multi-channel Spectral Estimation Affected by Sampling Jitters, in IEEE Northeast Bioengineering Conference (NEBC), 2007 Workshop Papers W4. M. Shan and N. Atanasov, A spatiotemporal model with visual attention for video classification, in Workshop on Articulated Model Tracking at RSS, 2017 W3. R. Ivanov, N. Atanasov, M. Pajic, I. Lee, and G. Pappas, Robust Localization Using Context-Aware Filtering, in Workshop on Multi-view Geometry in Robotics at RSS, 2015 W2. M. Lauri, N. Atanasov, G. Pappas, and R. Ritala, Active Object Recognition via Monte Carlo Tree Search, in Workshop on Beyond Geometric Constraints at ICRA, 2015 W1. M. Zhu, N. Atanasov, G. Pappas, and K. Daniilidis, Active Deformable Part Models Inference, in Workshop on Parts and Attributes at ECCV, 2014 Seminars and Talks 1. Panel on Advanced Robotic Imaging, The Indus Entrepreneurs, Oct. 2017. 2. Semantic Mapping and Mission Planning in Robotics, Machine Learning and Formal Methods Seminar, Dagstuhl, Aug. 2017. 3. Using Semantic Information in Robot Localization, Mapping, and Mission Planning, ATA Engineering, Aug. 2017. 3

Nikolay A. Atanasov Curriculum Vitae 4. Introduction to Robotics, COSMOS High-School Summer Program, Jul. 2017. 5. Metric-Semantic SLAM with Probabilistic Data Association, Artificial Intelligence Think Tank, hosted by the Gordon Engineering Leadership Center, Jun. 2017. 6. Acquiring Metric and Semantic Information Using Autonomous Robots at UC San Diego, Georgia Tech, MIT, University of Minnesota, UT Austin, Duke, Princeton, NYU, Stanford, ETH Zurich, BU, CMU, UC Berkeley, Feb.-Apr. 2016. 7. Active Information Acquisition with Mobile Robots at UC Berkeley, UCLA, University of Southern California, and California Institute of Technology, Feb. 2015. 8. Distributed Information Acquisition with Mobile Sensors, Workshop on Humans and Sensing in Cyber-Physical Systems, Robotics: Science and Systems (RSS) Conference, Berkeley, CA, July 2014. Research Group Ph.D. Students: Mo Shan (ECE UCSD) Tianyu Wang (ECE UCSD) Masters and Undergraduate Students: Siwei Guo (ECE UCSD) Youxing Wang (MAE UCSD) Yue Meng (ECE UCSD) Richard Du (ECE UCSD) Professional Activities Ph.D. Committees: 1. Stephen Chen, MAE PhD Senate Exam, UCSD, Jul. 2017 2. Daniel Yang, MAE PhD Senate Exam, UCSD, Dec. 2016 Program Committees: 1. Publications Chair, Robotics: Science and Systems (RSS), 2018. 2. Associate Editor, IEEE Int. Conf. on Robotics and Automation (ICRA), 2017, 2018. Workshop Organization: 1. Co-organizer, Workshop on Learning Perception and Control for Autonomous Flight: Safety, Memory, and Efficiency, Robotics: Science and Systems (RSS), Cambridge, MA, USA, July 2017. 2. Co-organizer, Workshop on Robot-Environment Interaction for Perception and Manipulation, Robotics: Science and Systems (RSS) Conference, Ann Arbor, MI, USA, June 2016. Government Activities: 1. NSF Panelist: CPS 2016, CISE 2017. 2. Workshop on Foundations of Intelligent Sensing, Action and Learning sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering, Philadelphia, PA, October 2015. 3. NSF Workshop on Learning, Perception and Control, Arlington, VA, August 2015. Reviewer: Journals: International Journal of Robotics Research; Elsevier Robotics and Autonomous Systems; Springer Autonomous Robots; IEEE Sensors Journal; IEEE Robotics and Automation Magazine; IEEE Robotics and Automation Letters; IEEE Trans. on Control of Network Systems; ASME Journal of Dynamic Systems, Measurement, and Control; Elsevier Computer Vision and Image Understanding; 4

Nikolay A. Atanasov Curriculum Vitae IEEE Trans. on Information Theory; Springer Journal of Intelligent and Robotic Systems; IEEE Trans. on Signal Processing Conferences: Robotics: Science and Systems (RSS), IEEE International Conference on Robotics and Automation (ICRA), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), European Conference on Computer Vision (ECCV), IEEE Conference on Decision and Control (CDC), American Control Conference (ACC), International Conference on Advanced Robotics (ICAR), IFAC Conference on Analysis and Design of Hybrid Systems, IEEE International Conference on Automation Science and Engineering, IEEE Multi-conference on Systems and Control, IEEE/SICE International Symposium on System Integration Society Membership: IEEE, Robotics and Automation Society 2014 - Present IEEE, Member 2007 - Present Phi Beta Kappa Society 2008 - Present IEEE, Communication Society 2012-2013 IEEE, Engineering in Medicine and Biology Society 2007-2008 American Society of Mechanical Engineers (ASME) 2007-2008 5

Biographical Sketch Sujit Dey Department of Electrical and Computer Engineering Tel. (858) 534-0750 University of California, San Diego Fax: (858) 822-3427 9500 Gilman Dr., La Jolla, CA 92093-0407 Email: dey@ece.ucsd.edu Web: http://esdat.ucsd.edu A. Professional Preparation B. Tech. 1985 India Institute of Technology, Kharagpur, India (Computer Science & Engineering) M.S. 1987 Southern Illinois University, Carbondale, IL (Computer Science) Ph.D. 1991 Duke University, Durham, NC (Computer Science) B. Appointments 2001 date Professor, ECE Department, University of California, San Diego 2016 date Director, Institute for the Global Entrepreneur, UC San Diego 2015 date Director, Center for Wireless Communications, UC San Diego 2013 2015 Faculty Director, von Liebig Center for Entrepreneurism, UC San Diego 2005 2008 Founder and CTO, Ortiva Wireless, Inc. 1998 2001 Assoc. Professor, ECE Department, University of California, San Diego 1997 1997 Senior Research Staff Member, C&C Research Labs NEC USA 1991 1996 Research Staff Member, C&C Research Labs NEC USA C. Publications related to Machine Learning and Data Sciences 1. W. Wei, Y. Lu, E. Rhoden, and S. Dey, "User Performance Evaluation and Real-Time Guidance in Cloud-Based Physical Therapy Monitoring and Guidance System," Springer Multimedia Tools and Applications, 2017. 2. P-H. Chiang, SPV Chiluvuri, S. Dey, TQ Nguyen, Forecasting of Solar Photovoltaic System Power Generation Using Wavelet Decomposition and Bias-Compensated Random Forest, Proceedings of Ninth Annual IEEE Green Technologies Conference, Denver, CO, March 2017, pp. 260-6. 3. C. Verma, M. Hart, S. Bhatkar, A. Parker-Wood, S. Dey, Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers, IEEE Access, Volume: 4, December 2015, pp. 204 215. 4. Yao Liu, S. Dey, F. Ulupinar, M. Luby, Yinian Mao, Deriving and validating user experience model for DASH video streaming, IEEE Transactions on Broadcasting, Vol 61 No 4, December 2015, pp. 651-65. 5. C. Verma, V. Mahadevan, N. Rasiwasia, G. Aggarwal, R. Kant, A. Jaimes, S. Dey, Construction and evaluation on ontological tag trees, Elsevier Expert Systems with Applications, Vol. 42 No 24, December 2015, pp. 9587-602. 6. D. Shen, Yao Lu, S. Dey, Motion Data Alignment for Real-Time Guidance in Avatar Based Physical Therapy Training System, Proceedings of IEEE 17th International Conference on E-Health Networking, Application & Services (IEEE HealthCom 2015), Boston, MA, October 2015, pp. 238-44. Best paper award runner-up. 7. C. Verma, S. Dey, Methods to obtain training videos for fully automated application specific classification, IEEE Access, Vol. 3, July 2015, pp. 1188-1205. 8. Yao Liu, S. Wang, and S. Dey, Content-Aware Modeling and Enhancing User Experience in Cloud Mobile Rendering and Streaming, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, special issue on Content-aware Visual Systems: Analysis, Streaming and Retargeting, Volume 4, No. 1, March 2014, pp. 43 56.

9. Y. Lu, Y. Liu and S. Dey, Cloud mobile 3D display gaming user experience model and optimization by asymmetric graphics rendering, IEEE Journal of Selected Topics in Signal Processing, Vol. 9, No 3, April 2015, pp. 1-16. 10. C. Verma and S. Dey, "Fully Automated Learning for Application-Specific Web Video Classification," Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Atlanta, GA, November 2013, pp. 307-314. D. Synergistic Activities 1. Research in Machine Learning and Data Sciences: My research group has been involved in the development and use of data science algorithms and architectures in diverse applications such as connected health, solar energy generation and distribution, online video classification, augmented and virtual reality, and massive MIMO systems. My research includes both machine learning and deep learning techniques suitable for the applications, as well as new hybrid cloud and edge architectures to ensure scalable and real-time solutions with high efficiency in computation and data communication. 2. Industry Alliances and Strategic Initiatives using Machine Learning and Data Sciences: After becoming Director of the UCSD Center for Wireless Communications (cwc.ucsd.edu) in 2015, I significantly increased CWC s industry partnerships, including the number of CWC member companies. While strengthening the traditional wireless research programs, I have led the creation of three new strategic programs: Smart City Innovation Program in partnership with the City of San Diego and 10 other corporate and non-profit organizations, the 5G Connected Health program with Kaiser Permanente as the lead corporate partner, and Smart Transportation Innovation Program in partnership with the Contextual Robotics Institute, UNIST, the City of Ulsan and the Institute for the Global Entrepreneur. Each of the above strategic initiatives involves extensive use of wireless sensors and applications to generate streaming data, and machine vision and machine learning techniques to provide predictive and prescriptive analytics. 3. Technology Commercialization and use of Big Data: Based on innovative wireless multimedia optimization technologies developed in my lab at UCSD, I founded Ortiva Wireless in 2004. Under my leadership as CTO, Ortiva successfully productized the technologies, which are currently deployed by Tier 1 Mobile Network Operators to enable the delivery of rich multimedia traffic like video with high quality user experience, while significantly enhancing the coverage and capacity of the networks. Ortiva was acquired by Allot Communications in 2012, where I served as the Chief Scientist, Mobile Networks. Under my leadership at Ortiva, data collected related to networks and usage were extensively used for understanding of wireless network conditions and deliver differentiated quality of service and user experience. 4. University R&D Commercialization and Entrepreneurship: After becoming the Director of the newly established Institute for the Global Entrepreneur (ige.ucsd.edu) in 2016, I have led the establishment of various entrepreneurial educational and training programs, a new Technology Accelerator program to facilitate the translation of impactful technologies from UCSD laboratories to the market, and a new global entrepreneur program. I am also the PI of the NSF I-Corps site program at UCSD. 5. Panels: I have participated in multiple technical panels, including on related topics like "Service Computing in Mobile World of Tomorrow: Reality v.s. Fantasy", and Embracing Cloud Based Mobility: Trends, Challenges, and Solutions at leading IEEE international conferences. 6. New Courses: I have developed several new courses at UCSD, including a new hardware-software design course for network and security protocols at the undergraduate level, and a graduate-level course on system-on-chip design. 7. Keynotes and Distinguished Lectures: I have presented several Keynotes on related topics like Mobile Cloud Computing to Enable Rich Media Applications, and Mobile Cloud Applications: Opportunities, Challenges and Directions, and a Distinguished Lecture on Wireless Cloud Computing to Enable Rich Mobile Multimedia Applications.

Professional preparation Massimo Franceschetti Biographical Sketch Ph.D. Electrical Engineering: California Institute of Technology, USA Oct. 2002 M.Sc. Electrical Engineering: California Institute of Technology, USA, June 1999 Laurea Computer Engineering: University of Naples, Italy, June 1997 Appointments July 2014 Present July 2008 June 2014 July 2004 June 2008 November 2002 June 2004 University of California, San Diego, Professor University of California, San Diego, Associate Professor University of California, San Diego, Assistant Professor University of California, Berkeley, Post-doctoral scholar Visiting positions and collaborations Fall 2005 Department of Mathematics, Vrije Universiteit Amsterdam, The Netherlands Summer 2005 Ecole Politecnique Federale de Lausanne (EPFL), Switzerland Summer 2006 University of Trento, Italy Selected publications [1] M. Franceschetti, R. Meester Random networks for communication. Cambridge, 2007. [2] H.Omidvar, M. Franceschetti Self-organized segregation on the grid. Proceedings of ACM-Principles of Distributed Computing, 2017. [3] L.Coviello, J. Fowler, M. Franceschetti Words on the web: non-invasive detection of emotional contagion in online social networks. Proceedings of the IEEE, 102(12), pp. 1911-1921, December 2014. [4] L.Coviello, Y. Sohn, A. Kramer, C. Marlow, M. Franceschetti, N.A. Christakis, J.H. Fowler Detecting emotional contagion in massive social networks. PLOS One, 9(3), e90315, March 2014. [5] L. Coviello, M. Franceschetti, M. Mc Cubbins, R. Paturi, A. Vattani, Human matching behavior in social networks: an algorithmic perspective PLOS ONE 7 (8): e41900, August 2012. [6] M. Franceschetti, M.D. Penrose, T. Rosoman, Strict inequalities of critical values in continuum percolation. Journal of statistical physics, 142(3), pp. 460-486, January 2011. [7] M. Franceschetti, R. Meester, Navigation in small world networks, a scale-free continuum model. Journal of applied probability, 43(4), pp. 1173-1180, December 2006. Synergistic Activities Associate Editor: IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2014-2017 Associate Editor: IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2013-2016 Associate Editor: IEEE TRANSACTIONS ON INFORMATION THEORY, 2009 2012 F 1

Guest Editor: Special issue on Models, Theory, and Codes for Relaying and Cooperation in Communication Networks, IEEE TRANSACTIONS ON INFORMATION THEORY, 2007. Special issue on Control and Communications, IEEE JOURNAL ON SPECIAL ISSUES IN COMMUNICATIONS, 2007. Special issue on Stochastic Geometry and Random Graphs for Wireless Networks, IEEE JOURNAL ON SPE- CIAL ISSUES IN COMMUNICATIONS, 2009. Technical Program Committee Chair: ACM Symposium on Mobile Ad-Hoc networks (Mobi-Hoc 2012) Spatial Stochastic Models of Wireless Networks Workshop (SpasWin 06) Professional Awards IEEE Control theory society Ruberti young researcher prize, 2012. IEEE Communications society best tutorial paper award, 2010. IEEE Antennas and propagation society S. A. Schelkunoff best paper award, 2005. Caltech Wilts prize for best thesis in electrical engineering, 2003. F 2

Vikash Gilja Department of Electrical and Computer Engineering University of California, San Diego 9500 Gilman Drive, MC 0407 La Jolla, CA 92093-0407 Phone:858-822-0685 tnel.ucsd.edu Professional Preparation Massachusetts Institute of Technology, Brain & Cognitive Sciences B.S., 2003 Cambridge, MA Massachusetts Institute of Technology, Cambridge, MA Electrical Engineering & Computer Science M.Eng./B.S., 2004 Stanford University, Stanford, CA Computer Science Ph.D./M.S, 2010 Academic Appointments 2013- present Assistant Professor, Electrical & Computer Engineering, University of California, San Diego, La Jolla, CA 2010-2013 Research Associate, Neural Prosthetics Translational Laboratory, Stanford University, Stanford, CA Publications Related to the application 1. Vikash Gilja*, Chethan Pandarinath*, Christine H. Blabe, Paul Nuyujukian, John D. Simeral, Anish A. Sarma, Brittany L. Sorice, Janos A. Perge, Beata Jarosiewicz, Leigh R. Hochberg, Jaimie M. Henderson, Krishna V. Shenoy (2015). Clinical translation of a high-performance neural prosthesis. Nature Medicine. 21, 1142 1145 2. Chethan Pandarinath, Vikash Gilja, Christine H. Blabe, Paul Nuyujukian, Anish A. Sarma, Brittany L. Sorice, Emad N. Eskandar, Leigh R. Hochberg, Jaimie M. Henderson, Krishna V. Shenoy (2015). Neural population dynamics in human motor cortex during movements in people with ALS. elife. e07436. 3. Cynthia A. Chestek, Vikash Gilja, Christine H. Blabe, Brett L Foster, Krishna V. Shenoy, Josef Parvizi, Jaimie M. Henderson (2013). Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas. Journal of Neural Engineering. 10:02602 (11pp). 4. Vikash Gilja*, Paul Nuyujukian*, Cynthia A. Chestek, John P. Cunningham, Byron M. Yu, Joline M. Fan, Mark M. Churchland, Matthew T. Kaufman, Jonathan C. Kao, Stephen I Ryu, Krishna V. Shenoy (2012) A High-Performance Neural Prosthesis Enabled by Control Algorithm Design. Nature Neuroscience. 15:1752-1757. 1

5. Vikash Gilja*, Cynthia A. Chestek*, Paul Nuyujukian, Justin D. Foster, Krishna V. Shenoy (2010) Autonomous head-mounted electrophysiology systems for freelybehaving primates. Current Opinion in Neurobiology. 20:676-686. Other Significant Publications 1. Vikash Gilja, Cynthia A. Chestek, Ilka Diester, Jaimie M. Henderson, Karl Deisseroth, Krishna V. Shenoy (2011) Challenges and opportunities for next-generation intracortically based neural prostheses. IEEE Transactions on Biomedical Engineering. 58:1891-1899. 2. Cynthia A. Chestek, Vikash Gilja, Paul Nuyujukian, Justin D. Foster, Joline M. Fan, Matthew T. Kaufman, Mark M. Churchland, Zuley Rivera-Alvidrez, John P. Cunningham, Stephen I Ryu, Krishna V. Shenoy (2011) Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. Journal of Neural Engineering.8:045005. 3. John P. Cunningham, Paul Nuyujukian, Vikash Gilja, Cynthia A. Chestek, Stephen I Ryu, Krishna V. Shenoy (2011) A closed-loop human simulator for investigating the role of feedback-control in brain-machine interfaces. Journal of Neurophysiology. 105:1932-1949. 4. Paul Nuyujukian *, Joline M. Fan *, Vikash Gilja, Paul S. Kalanithi, Cynthia A. Chestek, Krishna V. Shenoy (2011) Monkey models for brain-machine interfaces: The need for maintaining diversity. Proc. of the 33rd Annual International Conference IEEE EMBS. Boston, MA: 1301-1305. 5. Henrique Miranda, Vikash Gilja, Cynthia A. Chestek, Krishna V. Shenoy, Teresa H. Meng (2010) HermesD: A high-rate long-range wireless transmission system for simultaneous multichannel neural recording applications. IEEE Transactions on Biomedical Circuits and Systems. 4:181-191. Synergistic Activities Award Recognitions Hellman Fellowship 2016 Best Undergraduate Teaching Award 2015/2016, ECE Department, UCSD Best Graduate Teaching Award 2014/2015, ECE Department, UCSD National Professional Activities Topic Editor for Frontiers In Neuroscience special issue on brain machine interfaces and artificial intelligence, 2017- Coauthor of a US patent Conference organization Information Theory & Applications Workshop Session Organizer and Chair, 2014 University Activities UCSD Undergraduate Biomedical Engineering Society Project Team faculty advisor 2013-2015. 2

TODD L. HYLTON Ph: 858-525-5815 Email: toddhylton1@gmail.com SUMMARY OF EXPERIENCE Scientist, entrepreneur and technical manager with 30 years of experience in the semiconductor, computing, robotics, optical communications, data storage, and defense industries. Possesses broad technical and business background including research and development, start-ups, marketing, and government programs. EMPLOYMENT HISTORY & SELECTED CAREER ACHIEVEMENTS University of California San Diego, San Diego CA Executive Director of Contextual Robotics Institute 08/16 to present Initiated cross-disciplinary faculty group addressing technology and research for heathy aging capturing $10M support from leading technology company. Leading a campus-wide initiative to use the campus as a laboratory and testbed for next generation urban mobility services including autonomous vehicles, communications and traffic infrastructure, human-technology interaction development, and service model development Building corporate partners through memberships and sponsored research Brain Corporation, San Diego CA Executive Vice President of R&D and Technical Services 04/15 to 08/16 Initiated and managed a core group of scientists providing contract R&D services to government and commercial clients. Secured $1M DARPA contract to develop predictive, self-supervised, large scale, deep neural networks for visual tracking and robotic control Partnered / contracted with Sony, Panasonic and Acuity Brands to develop novel learning systems for IoT and robotics Released first version of BrainOS robotic software platform emphasizing embedded processing, learning and commercial application development Senior Vice President of Strategy 02/12 to 04/15 Developed integrated research, development, product and market strategies focused on realizing neurally inspired electronic systems for robotics. Managed and delivered Large R&D contract in biologically inspired vision systems for Qualcomm First Linux distribution on Qualcomm Snapdragon processors First robotics-focused development board and accessories featuring Qualcomm Snapdragon Processor ( bstem ) First learning-centric robotics software operating platform ( BrainOS ) Fully-integrated, low-cost, trainable robotics platform ( EyeRover ) Lead all executive staff meetings, planning sessions Built technical team of researchers and engineers Trained robot on stage at plenary session of Qualcomm s Uplinq developer conference Defense Advanced Research Projects Agency (DARPA), Arlington,VA Program Manager 5/07 to 1/12 Primary objective: Foster a revolution of understanding of intelligence, complex systems, and development of electronics technologies to build intelligent machines. Developed and managed a large portfolio of programs in neuromorphic computation, computer architecture, complex systems, foundations of intelligence, computational neuroscience, and small unmanned air vehicles including

TODD L. HYLTON, Resume, cont'd Page 2 of 7 Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) Physical Intelligence Biomimetic Computing Nano Air Vehicle (hummingbird robot) Additional programs developed and managed in vision based navigation for small UAVs, printed electronics, oxide electron devices, open architectures of mechanical systems, portable medical diagnostics, atmospheric methane phytoremediation, biologically self-decontaminating surfaces, and economics as complex dynamical systems Recognition for DARPA Programs SyNAPSE ACM 2009 Gordon Bell Prize (outstanding achievement in high performance computing) Scientific American s 10 World Changing Ideas December 2011 Washington Post Best Innovation Moments of 2011 (1 of 11), New York Times Feature The Future of Computing - 12/6/2011 Cover of Science - 02/01/2013 Cover of Science - 08/08/2015 Nano Air Vehicle Time Magazine 2011 Best Innovations Award and Cover 11/28/2011 Popular Mechanics 2011 Breakthrough Innovation Award October 2011 Popular Science 2011 Innovation of the Year Grand Award November 2011 Aviation Week 2011 Aeronautics/Propulsion Laureate Award Finalist Economics of Collective Value Wired Science 2011 Top 10 Scientific Discoveries (for mathematical models of global food markets) Science Applications International Corporation (SAIC), McLean,VA Director, Center for Advanced Materials and Nanotechnology 4/03 to 4/07 Built and managed a diverse group of scientists and engineers (18 people, $5M annual revenue) pursuing development and applications of materials and nanotechnology for government clients in the DoD, DoE, NIH and CIA. Led the development of technical capabilities, program wins and program execution in photovoltaics, optical tagging systems, cancer nanotechnology, flexible electronics, reconfigurable electromagnetic surfaces, holographic materials and other areas. Initiated new business at the National Cancer Institute providing technical and managerial support services for large cancer nanotechnology and cancer genome programs. Led the technical evaluation of internal venture capital investment in nanostructured structural materials company. Extensive work in proposal development and in creating teams of industry, academic, and government partners to bid and execute government programs. 4Wave, Inc., Sterling,VA President / Vice President of Technology / Director (ongoing) 7/00 to 4/03 Founded this $5M Engineering and R&D services business after acquisition of CVC by Veeco. Attracted large technology clients, including GE, IBM, Honeywell, and Nortel at a time when overall technology markets were in decline. Created an intellectual property plan resulting in 12 issued patents. Developed and sold first prototype "Biased Target Ion Beam Deposition" System. The technology captured in the system was a culmination of the VIP III program sponsored by DARPA. Invented compact optical multiplexer for 10 Gb/s Ethernet systems.

TODD L. HYLTON, Resume, cont'd Page 3 of 7 Commonwealth Scientific Corporation, Alexandria, VA / CVC, Inc., Alexandria, VA Chief Technical Officer / Director of Advanced Product Development 1/98 to 7/00 Led technical teams integrating ion beam etch and deposition products into the CSC/CVC product lines. Created $10 million in new sales with development of new etch product. Implemented 3 in-situ, thin film, process-monitoring technologies (Secondary Ion Mass Spectroscopy, Reflection Optical Monitoring, and Plasma Potential Control); the first implementations in the industry. The industry immediately purchased $7M in SIMS and PPC products over competitive offerings from the market leader. The technologies were subsequently adopted as standards in the industry. Championed the acquisition of CSC by CVC in 1999, thereby increasing market share of each company and enabling an Initial Public Offering and shareholder liquidity. Created a product management team to redefine the company's process equipment product line. Standardized, modularized, and reduced costs by 20%. Focused development efforts on 3 standard products: ion beam etch, ion beam deposition, and carbon film deposition platforms. Restructured disparate engineering and R&D functions into a single department with coherent goals and strategy. Hylton Research Services, LLC, San Jose, CA President 10/95 to 12/97 Consulted for leading data storage companies in the areas of thin film device processing and materials. Sold thin film processing services, thin film characterization services, magnetic measurements services, mask layouts, design of experiments, experimental analysis to research and development functions of leading data storage and process equipment companies. Educated marketing and technical personnel at CVC and Commonwealth Scientific Corporation on the building of Giant Magnetoresistive (GMR) deposition equipment used in the data storage industry over a period of approximately 6 months. Built the first Spin-Valve, GMR sensor structures on large wafers. CVC and CSC quickly became the market leader in GMR deposition equipment with combined revenues growing from approximately $50 million per year to approximately $100 million per year. Acquired Army contracts for development of a high sensitivity magnetoresistive sensor capable of fusing small munitions. Demonstrated sensor capable of navigating the Earth s magnetic field on a 1mm x 1mm chip. MicroUnity Systems Engineering Senior Scientist 1/95 to 10/95 Researched magnetic thin film materials and structures magnetic random access memory devices. Worked with semiconductor chip designers to evaluate magnetic memory concepts IBM, Storage Systems Division, San Jose, CA Staff Scientist/Engineer 3/91 to 12/94 Researched magnetic thin film materials and structures for magnetic recording head and media applications: magnetoresistance, giant magnetoresistance, and ferrite materials and devices. Built a laser ablation, thin film deposition laboratory for recording head and media materials development. Demonstrated the most sensitive and durable GMR structures at that time using novel materials and processing. Two patents were issued covering the invention. Recognized for work on GMR with a photo in IBM's annual report. Received major press coverage following publication of the work in Science magazine. Received Santa Clara University s award for most significant discovery in the field of magnetic storage. Recognized as a finalist in IBM's corporate-wide "New Business Opportunities Symposium" and appeared on a cover photo of IBM's Think magazine for magnetic sensor business plan.

TODD L. HYLTON, Resume, cont'd Page 4 of 7 Stanford University, Palo Alto, CA Research Associate 9/85 to 3/91 Conducted early experiments on the microwave losses and critical current densities in high temperature superconducting thin films of YBa 2Cu 3O x. Fabricated some of the first YBa 2Cu 3O x thin films by evaporation and co-sputtering techniques. Created a successful model for the electrical losses at grain boundaries in superconductors that explained the extreme sensitivity of microwave losses to the sample microstructure. The model improved the awareness of the scientific and technology communities of the importance of weakly superconducting grain boundaries. Created a successful model for the effect of point like defects in YBa 2Cu 3O x on flux pinning and critical current densities. Hughes Aircraft Co., Fullerton, CA Microwave and Antenna Engineer 6/83 to 9/85 Designed and tested a K-band, 2 meter diameter Cassegrain antenna system. Modeled electromagnetic diffraction and propagation in antenna systems. EDUCATION & ACADEMIC WORK Adjunct Professor of Physics, 2002-2007 Georgetown University Washington, DC Taught graduate course in Semiconductor Physics. Managed development of a polymer-mems based biofluidic glucose detector Ph.D., Applied Physics, 1991 Stanford University Palo Alto, CA Dissertation: Electrical Transport and Defect Structures in YBa 2Cu 3O x Thin Films B.S., Physics, 1983 Massachusetts Institute of Technology Cambridge, MA PROFESSIONAL ACTIVITIES & ASSOCIATIONS Industrial advisory board member University of Maryland, Institute for Systems Research, 2013-2016 Industrial advisory board member Georgetown University Department of Physics, 2006-2007 Led the nano-biotechnology working group for the Chesapeake Nanotechnology Initiative creating a strategic nanotechnology plan for the Maryland-Virginia-DC region, 2005 Served on Virginia Governor Tim Kaine s Transition Team Technology Committee, 2006 Testified before the US Senate Commerce Committee, Developments in Nanotechnology, 2/15/2006 Mindshare participant DC Metro area entrepreneurs association, 2001 Co-chair of the Virginia Electronics Industry Steering Committee sponsored by the Virginia Center for Innovative Technology, 1999 to 2001

TODD L. HYLTON, Resume, cont'd Page 5 of 7 PUBLICATIONS 1. J. Carini, A. Awasthi, W. Beyermann, G. Gruner, T. Hylton, K. Char, M.R. Beasley and A. Kapitulnik. "Millimeter-wave surface resistance measurements in highly oriented YBa2Cu3O7-d thin films," Phys. Rev. B37, 9726 (1988). 2. T.L. Hylton, A. Kapitulnik, M.R. Beasley, John P. Carini, L. Drabeck and George Gruner, "Weakly coupled grain model of high-frequency losses in high Tc superconducting thin films," Appl. Phys. Lett. 53, 1343 (1988). 3. L. Drabeck, J. P. Carini, G. Gruner, T. Hylton, K. Char and M.R. Beasley, "Power-law temperature dependence of the electrodynamic properties in oriented YBa2Cu3O7-d films," Phys. Rev. B39, 785 (1989). 4. T.L. Hylton, M.R. Beasley, A. Kapitulnik, J. P. Carini, L. Drabeck and G. Gruner, "Surface impedance studies of the high-tc oxide superconductors," IEEE Trans. Mag. 25, 810 (1989). 5. L. Drabeck, J. Carini, G. Gruner, T.L. Hylton, A. Kapitulnik and M.R. Beasley, "Surface impedance of high Tc superconductors," IEEE 1989 MTT Int. Microwave Sym. Dig., 2, 551 (1989). 6. K. Char, M.R. Hahn, T.L. Hylton, M.R. Beasley, T.H. Geballe and A. Kapitulnik, "Growth and properties of sputtered high-tc thin films, "IEEE Trans. Mag. 25, 2422 (1989). 7. T.L. Hylton and M.R. Beasley, "Effect of grain boundaries on magnetic field penetration in polycrystalline superconductors," Phys. Rev. B39, 9042 (1989). 8. T.L. Hylton and M.R. Beasley, "Flux pinning mechanisms in thin films of YBa2Cu3Ox," Phys. Rev. B41, 11669 (1990). 9. S. Laderman, R.C. Taber, R.D. Jacowitz, C.B. Eom, T.L. Hylton, M.R. Beasley and T.H. Geballe, "Defect structures and resistive loss at 10 GHz in c-axis aligned in-situ YBa2Cu3Ox films on MgO," Phys. Rev. B43, 2922 (1991). 10. M.R. Hahn, T.L. Hylton, K. Char, M.R. Beasley and A. Kapitulnik, "In-situ growth of YBa2Cu3Ox thin films by reactive cosputtering," J. Vac. Sci. Technol. A10, 82 (1992). 11. T.L. Hylton, M.R. Beasley, and R.C. Taber, "An iron-core magnetic inductance probe to measure critical current densities in superconducting thin films," Rev. Sci. Instrum. 63, 2248 (1992). 12. T L. Hylton, "Electrical transport and defect structures in YBa2Cu3Ox thin films," Ph.D. dissertation, Stanford University (1991). 13. T.L. Hylton, M.A. Parker and J.K. Howard, "Preparation and magnetic properties of epitaxial barium ferrite thin films on sapphire with in-plane, uniaxial anisotropy," Appl. Phys. Lett. 61, 867 (1992). 14. T.L. Hylton, M.A. Parker, K.R. Coffey and J.K. Howard, "Properties of epitaxial Ba-hexaferrite thin films on A, R and C-plane oriented sapphire substrates", J. Appl. Phys. 73, 6257 (1993). 15. T.L. Hylton, "Limitations of magnetoresistive sensors based on the giant magnetoresistive effect in granular magnetic composites, " Appl. Phys. Lett. 62, 2431 (1993). 16. M.A. Parker, T.L. Hylton and J.K. Howard, "Solid phase heteroepitaxy of barium ferrite on sapphire," Mat. Res. Soc. Symp. Proc. (1992). 17. M.A. Parker, K.R. Coffey, T.L. Hylton, J.K. Howard, The microstructure and giant magnetoresistance of NiFeAg thin films, Mat. Res. Soc. Symp. Proc. 313, 85 (1993).

TODD L. HYLTON, Resume, cont'd Page 6 of 7 18. K.R. Coffey, T.L. Hylton, M.A. Parker, J.K. Howard, Granular Giant Magnetoresistance at Low Fields in Bilayer Thin Films, Appl. Phys. Lett. 63, 1579 (1993). 19. T.L. Hylton, K.R. Coffey, M.A. Parker, J.K. Howard, Giant magnetoresistance at low fields in discontinuous multilayers of NiFe/Ag, Science 261, 1021 (1993). 20. T.L. Hylton, K.R. Coffey, M.A. Parker, J.K. Howard, Low field giant magnetoresistance in discontinuous magnetic multilayers, J. Appl. Phys. 75, 7058 (1994). 21. M.A. Parker, T.L. Hylton, K.R. Coffey, J.K. Howard, Microstructural origin of giant magnetoresistance in a new sensor structure based on NiFe/Ag discontinuous multilayer thin films, J. Appl. Phys. 75, 6382 (1994). 22. T.L. Hylton, M.A. Parker, M. Ullah, K.R. Coffey, R. Umphress, J.K. Howard, Ba-ferrite thin film media for high density longitudinal recording, J. Appl. Phys. 75, 5960 (1994). 23. T.L. Hylton, M.A. Parker, K.R. Coffey, J.K. Howard, R. Fontana and C. Tsang, Magnetostatically induced giant magnetoresistance in patterned NiFe/Ag multilayer thin films, Appl. Phys. Lett. 67, 1154 (1995). 24. J.K. Spong, V.S. Speriosu, R.E. Fontana, M.M. Dovek and T.L. Hylton, Giant Magnetoresistive Spin-Valve Bridge Sensor, IEEE Trans. Magn. 32, 366 (1996). 25. M.A. Parker, K.R. Coffey, J.K. Howard, C.H. Tsang, R.E. Fontana and T.L. Hylton, Overview of progress in giant magnetoresistive sensors based on NiFe/Ag multilayers, IEEE Trans. Magn. 32, 142 (1996). 26. V.V. Zhurin, H.R. Kaufman, J.R. Kahn, T.L. Hylton, Biased Target Deposition, J. Vac. Sci. Technol. A 18, 37 (2000). 27. T.L. Hylton, D.A. Baldwin. B. Ciorneiu, O. Escorcia, J. Son, Thin Film Processing by Biased Target Ion Beam Deposition, IEEE Trans. Magn. 36, 2996 (2000). 28. A.P. Gadre, Y.N. Srivastava, J.A. Garra, A.J. Nijdam, A.H. Monica, M.C. Cheng, C. Luo, T.W. Schneider, T.J. Long, R.C. White, T.L. Hylton, M. Paranjpe, J.F. Currie, Multi-polymer Fabrication of a Biofluidic Transdermal Sampling Device, accepted at MicroTAS2003, April 2003. 29. H. N. G. Wadley, X. W. Zhou, J. J. Quan, T.L. Hylton, D. Baldwin, "Biased Target Ion Beam Deposition of GMR Multilayers," Non-Volatile Memory Technology Symposium 2003, November 2003. 30. Y. N. Srivastava, A.P. Gadre, T. Hylton, A. Monica, M. Paranjape and J. F. Currie, A Polymethylmethacrylate membrane for fluid encapsulation and release in microfluidic systems, Journal of Vacuum Science and Technology A, 22(3) (2004). 31. Todd Hylton, New Developments and Future Prospects for Small UAVs, IQT Quarterly, 3(1), Summer 2011. 32. Filip Piekniewski, Patryk Laurent, Csaba Petre, Micah Richert, Dimitry Fisher, Todd Hylton, Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network. arxiv:1607.06854 33. Micah Richert, Dimitry Fisher, Filip Piekniewski, Eugene M. Izhikevich, Todd L. Hylton, Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback, arxiv:1608.06277

TODD L. HYLTON, Resume, cont'd Page 7 of 7 PATENTS 6,843,891 - Apparatus for sputter deposition 6,819,871 - Multi-channel optical filter and multiplexer formed from stacks of thin-film layers 6,723,209 - System and method for performing thin film deposition or chemical treatment using an energetic flux of neutral reactive molecular fragments, atoms or radicals 6,689,255 - System and method for making thin-film structures using a stepped profile mask 6,682,634 - Apparatus for sputter deposition 6,679,976 - System and method for performing sputter deposition with multiple targets using independent ion and electron sources and independent target biasing with DC pulse signals 6,610,179 - System and method for controlling deposition thickness using a mask with a shadow that varies with respect to a target 6,488,821 - System and method for performing sputter deposition using a divergent ion beam source and a rotating substrate 6,419,803 - System and method for making thin-film structures using a stepped profile mask 6,419,802 - System and method for controlling deposition thickness by synchronously varying a sputtering rate of a target with respect to a position of a rotating substrate 6,402,905 - System and method for controlling deposition thickness using a mask with a shadow that varies along a radius of a substrate 6,402,904 - System and method for performing sputter deposition using independent ion and electron sources and a target biased with an a-symmetric bi-polar DC pulse signal 6,402,901 - System and method for performing sputter deposition using a spherical geometry 6,402,900 - System and method for performing sputter deposition using ion sources, targets and a substrate arranged about the faces of a cube 6,016,241 - Magnetoresistive sensor utilizing a granular magnetoresistive layer 5,629,682 - Magnetic recorder having MR read and inductive write capability 5,492,775 - Barium ferrite thin film for longitudinal recording 5,476,680 - Method for manufacturing granular multilayer magnetoresistive sensor 5,452,163 - Multilayer magnetoresistive sensor

Tara Javidi, Professor University of California Department of Electrical and Computer Engineering San Diego, CA 92093-0407 Biographical Sketch (i) Professional Preparation Sharif University of Technology, Tehran, Iran Electronics BS, 1996 University of Michigan, Ann Arbor Electrical Engineering & Computer Science: Systems MS, 1998 University of Michigan, Ann Arbor Applied Mathematics MS, 1999 University of Michigan, Ann Arbor Electrical Engineering & Computer Science PhD, 2002 (ii) Appointments July 2010-Present Associate Professor University of California, San Diego Jan 2005-June 2010 Assistant Professor University of California, San Diego Sept 2002-Dec 2004 Assistant Professor University of Washington, Seattle (iii) Publications Publications [Top Most Related] 1. M. Naghshvar and T. Javidi. Active Sequential Hypothesis Testing. Annals of Statistics. Vol 41. No 6. December 2013 2. M. Naghshvar, T. Javidi, and K. Chaudhuri. Bayesian Active Learning With Non-Persistent Noise. IEEE Transactions on Information Theory. Vol 61. Issue: 7. July 2015 3. Songbai Yan, Kamalika Chaudhuri and Tara Javidi. Active Learning from Imperfect Labelers. In Proceedings of Neural Information Processing Systems (NIPS), December 2016 4. Y. Kaspi, O. Shayevitz, and T. Javidi. Searching with Measurement Dependent Noise. Accepted for publication in IEEE Transactions on Information Theory 5. A. Lalitha, T. Javidi, and A.D. Sarwate. Social Learning and Distributed Hypothesis Testing. Accepted for publication in IEEE Transactions on Information Theory Publications [Other] 6. Y. Lu, T. Javidi, and S. Lazebnik. Adaptive Object Detection Using Adjacency and Zoom Prediction. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2016. [spotlight] 7. A. Lalitha, T. Javidi, and A.D. Sarwate. Social Learning and Distributed Hypothesis Testing. Accepted for publication in IEEE Transactions on Information Theory 8. A. Lalitha and T. Javidi. Reliability of Sequential Hypothesis Testing Can Be Achieved by an Almost-Fixed-Length Test. in Proceedings of the IEEE International Symposium on Information Theory (ISIT), July 2016 9. D. Klein, P. Lee, K. Morgansen, T. Javidi, Integration of Communication and Control using Discrete Time Kuramoto Models for Multivehicle Coordination over Broadcast Networks, IEEE Journal of Selected Areas in Communiations, v. 26. no. 4. May 2008 10. Y. Lu, A. Kumar, S. Zhai, Y. Cheng, T, Javidi, R. Feris. Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification. in Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (spotlight)

(iv) Synergistic Activities Research Affliations Center for Machine-Integrated Computing and Security (MICS), UCSD [founding codirector} Information Theory and Application Center (ITA), UCSD Center for Wireless Communications (CWC), UCSD Californian Institute for Telecommunications and Information Technology (CALIT2), UCSD Center for Networked Systems (CNS), UCSD Cross Disciplinary Collaborations Cricical Gender Studies, UCSD Ethnic Studies Department (Affiliate Faculty) Computer Science and Engineering Department, UCSD (Affiliate Faculty) Center for Engineering Learning and Teaching (CELT), University of Washington, Seattle Mentoring and Advising Outreach & Mentoring Committees, Member, IEEE Information Theory Society Society of Women Engineers, Faculty Advisor, UCSD Student Chapter (2005-present) National Society of Black Engineers, UCSD student Chapter (2010-11) Women in Science and Engineering (WISE), University of Michigan

Biographical Sketch: Professor Andrew B. Kahng, UC San Diego Professional Preparation Harvard College, Cambridge, MA, Applied Mathematics (Physics), A.B., 1984 University of California at San Diego, La Jolla, CA, Computer Science, M.S., 1986 University of California at San Diego, La Jolla, CA, Computer Science, Ph.D., 1989 Appointments University of California at San Diego, Depts. of CSE and ECE Professor, 2001- present University of California at Los Angeles, Dept. of CS Professor and Vice-Chair, 1998-2000 University of California at Los Angeles, Dept. of CS Associate Professor, 1994-1998 University of California at Los Angeles, Dept. of CS Assistant Professor, 1989-1994 Blaze DFM, Inc., Sunnyvale, CA Chief Technologist, 2004-2006 Cadence Design Systems, Inc., San Jose, CA Visiting Scientist, 1995-1997 Professional Highlights Fellow, IEEE (2010) Fellow, ACM (2012) UCSD Endowed Chair in High-Performance Computing, 2010-present 18 Best Paper nominations, 6 Best Paper awards Author of 3 books and over 450 journal and conference papers (h-index 76, 23000+ citations) 33 issued U.S. patents 26 Ph.D. graduates (3 currently at Qualcomm) Service as general chair of DAC, ISQED, ISPD (founder), SLIP (co-founder), EDPS, etc. Research interests: IC physical design and performance analysis, the IC design-manufacturing interface, combinatorial algorithms and optimization, and the roadmapping of systems and technology Long-format CV: http://vlsicad.ucsd.edu/~abk/kahng_cv.pdf

E. Biographical Sketch: Young-Han Kim Department of Electrical and Computer Engineering University of California, San Diego 9500 Gilman Drive, Mail Code 0407 La Jolla, CA 92093-0407 Tel: (858) 534-4254 Fax: (858) 534-1225 Email: yhk@ucsd.edu Web: http://circuit.ucsd.edu/~yhk/ Education 1996 Seoul National University, Seoul, Korea, B.S. in Electrical Engineering. 2001 Stanford University, Stanford, CA, M.S. in Electrical Engineering. 2006 Stanford University, Stanford, CA, M.S. in Statistics. 2006 Stanford University, Stanford, CA, Ph.D. in Electrical Engineering. Appointments 7/2017 present Professor, Department of Electrical and Computer Engineering, UCSD. 7/2012 6/2017 Associate Professor, Department of Electrical and Computer Engineering, UCSD. 7/2006 6/2012 Assistant Professor, Department of Electrical and Computer Engineering, UCSD. Publications Most Closely Related to the Proposed Project 1. Jiantao Jiao, Lei Zhao, Haim H. Permuter, Young-Han Kim, and Tsachy Weissman, Universal estimation of directed information, IEEE Trans. Inf. Theory, vol. 59, no. 10, pp. 6220 6242, October 2013. 2. Haim H. Permuter, Young-Han Kim, and Tsachy Weissman, Interpretations of directed information in portfolio theory, data compression, and hypothesis testing, IEEE Trans. Inf. Theory, vol. 57, no. 6, pp. 3248 3259, June 2011. 3. Yuzhe Jin, Young-Han Kim, and Bhaskar D. Rao, Support recovery of sparse signals via multiple-access communication techniques, IEEE Trans. Inf. Theory, vol. 57, no. 12, pp. 7877 7892, December 2011. 4. Fatemeh Arbabjolfaei and Young-Han Kim, Fundamentals of index coding, submitted to Found. Trends Commun. Inf. Theory, 2017, 139 pp. Other Significant Publications 1. Abbas El Gamal and Young-Han Kim, Network Information Theory, Cambridge University Press, 2011. 2. Young-Han Kim, Feedback capacity of stationary Gaussian channels, IEEE Trans. Inf. Theory, vol. 56, no. 1, pp. 57 85, January 2010. 3. Sung Hoon Lim, Young-Han Kim, Abbas El Gamal, and Sae-Young Chung, Noisy network coding, IEEE Trans. Inf. Theory, vol. 57, no. 5, pp. 3132 3152, May 2011. 4. Paolo Minero, Sung Hoon Lim, and Young-Han Kim, A unified approach to hybrid coding, IEEE Trans. Inf. Theory, vol. 61, no. 4, pp. 1509 1523, April 2015. 5. Sung Hoon Lim, Kwang Taik Kim, and Young-Han Kim, Distributed decode forward for relay networks, IEEE Trans. Inf. Theory, vol. 63, no. 7, pp. 4103 4118, July 2017.

Synergistic Activities 1. Development of a new course (UCSD ECE 255C) on network information theory. The lecture notes, developed in close collaboration with Abbas El Gamal (Stanford), is available to the general public at http://arxiv.org/abs/1001.3404/ and has been used in several institutions including Stanford University, UCSD, the Chinese University of Hong Kong, UC Berkeley, Tsinghua University, Seoul National University, Princeton University, and McGill University. 2. Organization for the UCSD Information Theory and Applications (ITA) Workshop, 2007 present. In addition to the regular technical program, Young-Han Kim has organized the Graduation Day event at the ITA Workshop, in which graduate students and postdoctoral scholars (including many minority students) present mini job talks to the broad community. 3. Invited talks at the IEEE Vehicular Technology Society San Diego Chapter, 2007, 2009 and UCSD Jacobs School Research Expo, 2009. These talks on feedback communication, sparse signal processing, and network information theory were delivered to a broader audience mostly consisting of local industry engineers, with the primary goal of disseminating research results from NSF sponsored projects. 4. Freshman seminars, 2011 and 2015. These seminars on puzzles, information, and codes expose young students to basic concepts of information theory and coding theory through interesting puzzles involving hats, coins, eggs, bad wine, etc. 2

Farinaz Koushanfar University of California, San Diego farinaz@ucsd.edu 9500 Gilman Drive, Mail Code 0407 Phone: +1-858-534-3294 La Jolla, CA 92093-0407 Fax: +1-858-534-5909 Citizenship: United States aceslab.org Research Interests Embedded systems, machine learning, security and trust, hardware, customizable computing, design automation Education Ph.D. in Electrical Engineering and Computer Science 12/2005 University of California, Berkeley M.A. in Statistics 5/2005 University of California, Berkeley M.S. in Electrical Engineering (thesis jointly done in Computer Science) 12/2000 University of California, Los Angeles B.S. in Electrical Engineering 12/1998 Sharif University of Technology, Tehran, Iran Professional Experience Professor and Henry Booker Faculty Scholar University of California San Diego (UCSD), Electrical & Computer Engineering 11/2015-Present Professor 09/2015-12/2015 Rice University, Electrical & Computer Engineering Associate Partner Intel Collaborative Research Institute for Secure Computing 07/2014-Present Principal Director, Texas Instruments (TI) DSP Leadership 09/2008-12/2015 Rice University Assistant Professor and (Tenured) Associate Professor 07/2006-09/2015 Rice University, Electrical & Computer Engineering Visiting Assistant Professor 01/2010-07/2010 MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) Visiting Assistant Professor/ CSL Fellow 10/2005-07/2006 Coordinated Science Lab (CSL), University of Illinois at Urbana-Champaign Intel Open Collaborative Research Fellow 08/2003-08/2004 Intel Research, Berkeley Lab Awards and Honors ICCAD Ten Year Retrospective Most Influential Paper Award....................... 2017 Henry Booker Faculty Scholarship, UCSD.................................. 2016 Cisco IoT Security Grand Challenge Award................................. 2014 National Academy of Science (NAS) Kavli Foundation Fellowship.................... 2012 Army Research Office (ARO) Young Investigator Program (YIP) Award................. 2012 ACM SIGDA Outstanding New Faculty Award (ONFA).......................... 2011 Presidential Early Career Award for Scientists and Engineers (PECASE)................. 2010 Office of Naval Research (ONR) Young Investigator Program (YIP) Award............. 2009-2012 National Science Foundation (NSF) CAREER Award......................... 2007-2011 Young Faculty Award, Defense Advanced Research Projects Agency (DARPA)........... 2007-2009 MIT Technology Review TR-35 Award (World s Top Innovators Under 35)................ 2008 Cyber Security Awareness (CSAW) Best Applied Security Paper Award, 2nd place........... 2013

National Academy of Engineering (NAE) Frontiers of Engineering................... 2009 INTEL Open Collaborative Research (OCR) Fellowship Award................... 2003-2004 Best Student Paper Award, ACM SIGMOBILE (Mobicom)......................... 2001 National Science Foundation (NSF) Graduate Student Research Fellowship............ 2000-2003 Peer-Reviewed Journal Articles 33. B. D. Rouhani, A. Mirhoseini, and F. Koushanfar, RISE: An Automated Framework for Real-Time Intelligent Video Surveillance on FPGA, ACM Transactions on Embedded Computing Systems (TECS), 2017. 32. M. S. Riazi, M. Samragh, and F. Koushanfar, CAMsure: Secure Content-Addressable Memory for Approximate Search, ACM Transactions on Embedded Computing Systems (TECS), 2017. 31. M. S. Riazi, E. M. Songhori, A. - R. Sadeghi, T. Schneider, and F. Koushanfar, Toward Practical Secure Stable Matching, Proceedings on Privacy Enhancing Technologies, vol. 2017, issue 1, 01/2017. 30. B. D. Rouhani, A. Mirhoseini, E. Songhori, and F. Koushanfar. Automated Analysis of Streaming Big and Dense Data on Reconfigurable Platforms. ACM Transactions on Reconfigurable Technology and Systems (TRETS), vol. 10, issue 1, Dec 2016. 29. A. Mirhoseini, B. D. Rouhani, E. M. Songhori, and F. Koushanfar, Chime: Checkpointing long computations on intermittently energized IoT devices, IEEE Transactions on Multi-Scale Computing Systems (TMSCS), vol. 2, issue 99, Jan 2016. 28. S. U. Hussain, M. Majzoobi, and F. Koushanfar, A Built-In-Self-Test Scheme for Online Evaluation of Physical Unclonable Functions and True Random Number Generators, IEEE Transactions on Multi-Scale Computing Systems (TMSCS), vol. 2, no. 99, Jan 2016. 27. S. Chung, J. Kong, and F. Koushanfar, An Energy-efficient Last-level Cache Architecture for Process Variation-tolerant 3D Microprocessors, IEEE Trans. on Computers, vol. 64, no. 9, pp. 2460 2475, Sept 2015. 26. A. Mirhoseini, M. Potkonjak, and F. Koushanfar, Phase Change Memory Write Cost Minimization by Data Encoding, IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), Special Issue on Computing in Emerging Technologies, vol. 5, no. 1, pp. 51 63, Mar 2015. 25. A. N. Nowroz, K. Hu, F. Koushanfar, and S. Reda, Novel Techniques for High-sensitivity Hardware Trojan Detection using Thermal and Power Maps, IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 33, no. 12, pp. 1792 1805, Dec 2014. 24. M. Rostami, F. Koushanfar, and R. Karri, A Primer on Hardware Security: Models, Methods, and Metrics, Proceedings of the IEEE, vol. 102, no. 8, pp. 1283 1295, Aug 2014. 23. C. Herder, M.-D. Yu, F. Koushanfar, and S. Devadas, Physical Unclonable Functions and Applications: A Tutorial, Proceedings of the IEEE, vol. 102, no. 8, pp. 1126 1141, Aug 2014. 22. J. Kong and F. Koushanfar, Processor-based Strong Physical Unclonable Functions with Aging-based Response Tuning, IEEE Transactions on Emerging Topics in Computing (JETC), vol. 2, pp. 16 29, Mar 2014. 21. M. Rostami, M. Majzoobi, F. Koushanfar, D. Wallach, and S. Devadas, Robust and Reverse-Engineering Resilient PUF Authentication and Key-Exchange by Substring Matching, IEEE Transactions on Emerging Topics in Computing (JETC), vol. 2, no. 1, pp. 37 49, Mar 2014. 20. A. Munir, A. Gordon-Ross, S. Ranka, and F. Koushanfar, A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multi-core Embedded Systems, Elsevier Journal of Parallel and Distributed Computing (JPDC), vol. 74, no. 1, pp. 1872 1890, Jan 2014. 19. A. Munir, F. Koushanfar, A. Gordon-Ross, and S. Ranka, High-performance Optimizations on Tiled manycore Embedded Systems: A Matrix Multiplication Case Study, The Journal of Supercomputing, vol. 66, no. 1, pp. 431 487, Apr 2013. 18. Y.-K. Chen, A.-Y. Wu, M. A. Bayoumi, and F. Koushanfar, Editorial: Low-power, Intelligent, and Secure Solutions for Realization of Internet of Things, IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), vol. 3, no. 1, pp. 1 4, Mar 2013.

17. N. Kiyavash, F. Koushanfar, T. P. Coleman, and M. Rodrigues, A Timing Channel Spyware for the CSMA/CA Protocol, IEEE Transactions on Information Forensics and Security (TIFS), vol. 8, no. 3, pp. 477 487, Mar 2013. 16. M. Majzoobi, J. Kong, and F. Koushanfar, Low-power Resource Binding by Postsilicon Customization, ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 18, no. 2, p. 26:1 26:22, Mar 2013. 15. S. Wei, A. Nahapetian, M. Nelson, F. Koushanfar, and M. Potkonjak, Gate Characterization using Singular Value Decomposition: Foundations and Applications, IEEE Transactions on Information Forensics and Security (TIFS), vol. 7, no. 2, pp. 765 773, Apr 2012. 14. F. Koushanfar, Provably Secure Active IC Metering Techniques for Piracy Avoidance and Digital Rights Management, IEEE Transactions on Information Forensics and Security (TIFS), vol. 7, no. 1, pp. 51 63, Feb 2012. 13. F. Koushanfar and A. Mirhoseini, A Unified Framework for Multimodal Submodular Integrated Circuits Trojan Detection, IEEE Transactions on Information Forensics and Security (TIFS), vol. 6, no. 1, pp. 162 174, Mar 2011. 12. M. Majzoobi and F. Koushanfar, Time-bounded Authentication of FPGAs, IEEE Transactions on Information Forensics and Security (TIFS), vol. 6, no. 3, pp. 1123 1135, Aug 2011. 11. J. A. Roy, F. Koushanfar, and I. L. Markov, Ending Piracy of Integrated Circuits, IEEE Computer, vol. 43, no. 10, pp. 30 38, Oct 2010. 10. F. Koushanfar, M. Majzoobi, and M. Potkonjak, Nonparametric Combinatorial Regression for Shape Constrained Modeling, IEEE Trans. On Signal Processing, vol. 58, no. 2, pp. 626 637, Aug 2010. 9. M. Tehranipoor and F. Koushanfar, Guest editors introduction: Confronting the Hardware Trustworthiness Problem, IEEE Design & Test of Computers, vol. 27, no. 1, pp. 8 9, Jan 2010. 8. M. Tehranipoor and F. Koushanfar, A Survey of Hardware Trojan Taxonomy and Detection, in IEEE Design & Test of Computers, vol. 27, no. 1, Jan 2010, pp. 10 25. 7. M. Majzoobi, F. Koushanfar, and M. Potkonjak, Techniques for Design and Implementation of Secure Reconfigurable PUFs, ACM Trans. on Reconfigurable Technology and Systems (TRETS), vol. 2, no. 1, p. 5:1 5:33, Mar 2009. 6. F. Koushanfar, A. Davare, D.T. Nguyen, A.L. Sangiovanni-Vincentelli, and M. Potkonjak, Techniques for Maintaining Connectivity in Wireless Ad-hoc Networks Under Energy Constraints, ACM Trans. on Embedded Computing Systems (TECS), vol. 6, no. 3, 2007. 5. S. Megerian, F. Koushanfar, M. Potkonjak, and M. Srivastava, Worst and Best-case Coverage in Sensor Networks, IEEE Trans. on Mobile Computing, vol. 4, pp. 84 92, Jan 2005. 4. F. Koushanfar, I. Hong, and M. Potkonjak, Behavioral Synthesis Techniques for Intellectual Property Protection, ACM Trans. Design Automation of Electronic Systems (TODAES), vol. 10, no. 3, pp. 523 545, Jul 2005. 3. J. Wong, F. Koushanfar, S. Megerian, and M. Potkonjak, Probabilistic Constructive Optimization Techniques, IEEE Trans. of Computer Aided Designs (TCAD), vol. 23, pp. 859 868, 2004. 2. S. Megerian, F. Koushanfar, G. Qu, G. Veltri, and M. Potkonjak, Exposure in Wireless Sensor Networks: Theory and Practical Solutions, ACM Journal of Wireless Networks, vol. 8, pp. 443 454, Sep 2002. 1. F. Koushanfar, D. Kirovski, I. Hong, M. Potkonjak, and M. Papaefthymiou, Symbolic Debugging of Embedded Hardware and Software, IEEE Transactions on Computer-Aided Design, vol. 20, pp. 392 401, Mar 2001. Conference Proceedings 104. M. Samragh, M. Ghasemzadeh, and F. Koushanfar, Customizing Neural Networks for Efficient FPGA Implementation, IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2017. 103. B. D. Rouhani, A. Mirhoseini, and F. Koushanfar, Deep 3 : Leveraging Three Levels of Parallelism for Efficient Deep Learning, Design Automation Conference (DAC): 2017.

102. Riazi, S. M., E. M. Songhori, and F. Koushanfar, PriSearch: Efficient Search on Private Data, Design Automation Conference (DAC), 2017. 101. G. Dessouky, F. Koushanfar, A-R. Sadeghi, T. Schneider, S. Zeitouni, M. Zohner, Pushing the communication barrier in secure computation using lookup tables, Network and Distributed System Security Symposium (NDSS), 2017. 100. M. Samragh, M. Imani, F. Koushanfar, and T. Rosing, LookNN: Neural Network with No Multiplication, Design Automation and Test in Europe (DATE), 2017. 99. A. Mirhoseini, B.D. Rouhani, E. M. Songhori, and F. Koushanfar, ExtDict: Extensible Dictionaries for Data- and Platform-Aware Large-Scale Learning, International Parallel & Distributed Processing Symposium (IPDPS) Workshop: IEEE, 2017. Best Student Paper Award 98. B. D. Rouhani. M. Riazi, and F. Koushanfar, DeepSecure: Scalable Provably-Secure Deep Learning, IEEE International Conference on High Performance Computing and Communications (HPCC), 2017. 97. B. D. Rouhani, A. Mirhoseini, and F. Koushanfar, TinyDL: Just-In-Time Deep Learning Solution For Constrained Embedded Systems, International Symposium on Circuits and Systems (ISCAS): IEEE, 2017. 96. A. Mirhoseini, B. D. Rouhani, E. M. Songhori, and F. Koushanfar, Perform-ML: Performance Optimized Machine Learning by Platform and Content Aware Customization, Design Automation Conference (DAC), 2016. 95. S. U. Hussain, and F. Koushanfar, Privacy Preserving Localization for Smart Automotive Systems, Design Automation Conference (DAC), 2016. 94. E. M. Songhori, S. Zeitouni, G. Dessouky, T. Schneider, A. - R. Sadeghi, and F. Koushanfar, GarbledCPU: A MIPS Processor for Secure Computation in Hardware, Design Automation Conference (DAC), 2016. 93. T. Abera, N. Asokan, L. Davi, F. Koushanfar, A. Paverd, A. - R. Sadeghi, and G. Tsudik, INVITED: Things, Trouble, Trust: On Building Trust in IoT Systems, Design Automation Conference (DAC), 2016. 92. Y. Zhang, and F. Koushanfar, Robust Privacy-Preserving Fingerprint Authentication, IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2016. 91. M. S. Riazi, N. K. R. Dantu, V. L. N. Gattu, and F. Koushanfar, GenMatch: Secure DNA Compatibility Testing, IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2016. 90. A. Mirhoseini, A. - R. Sadeghi, and F. Koushanfar, CryptoML: Secure Outsourcing of Big Data Machine Learning Applications, IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2016. 89. F. Koushanfar, A. Mirhoseini, G. Qu, and Z. Zhang, DA Systemization of Knowledge: A Catalog of Prior Forward-Looking Initiatives, (invited) IEEE/ACM International Conference on Computer-Aided Design (IC- CAD), pp.. 255 262, 2015. 88. A. B. Kahng, and F. Koushanfar, Evolving EDA Beyond its E-Roots: An Overview, (invited) IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 247-254, 2015. 87. E. Songhori, S.U. Hussain, A-R. Sadeghi, T. Schneider, and F. Koushanfar, TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits, in IEEE Symposium on Security and Privacy (S&P), pp. 411 428, 2015. 86. B. Rouhani, E. Songhori, A. Mirhoseini, and F. Koushanfar, SSketch: An Automated Framework for Streaming Sketch-based Analysis of Big Data on FPGA, in International Symposium on Field-Programmable Custom Computing Machines (FCCM), in press, 2015. 85. M. Miettinen, M. Sobhani, T.D. Nguyen, J. Rios, S. Yellapantula, N. Asokan, A-R. Sadeghi, and F. Koushanfar, I Know Where You are: Proofs of Presence Resilient to Malicious Provers, in ACM Symposium on Information, Computer and Communications Security (ASIACCS), pp. 567 577, 2015. 84. E. Songhori, S.U. Hussain, A-R. Sadeghi, and F. Koushanfar, Compacting privacy-preserving k-nearest neighbor search using logic synthesis, in Design Automation Conference (DAC), no. 36, 2015. 83. E. Songhori, A. Mirhoseini, F. Koushanfar, AHEAD: Automated Framework for Hardware Accelerated Iterative Data Analysis, in Design, Automation & Test in Europe (DATE), pp. 942 947, 2015.

82. S. U. Hussain, S. Yellapantula, M. Majzoobi, and F. Koushanfar, BIST-PUF: Online, Hardware-based Evaluation of Physically Unclonable Circuit Identifiers, in International Conference on Computer-Aided Design (ICCAD), Nov 2014, pp. 162 169. 81. D. Shahrjerdi, J. Rajendran, S. Garg, F. Koushanfar, and R. Karri, Shielding and Securing Integrated Circuits with Sensors, in International Conference on Computer-Aided Design (ICCAD), Nov 2014, pp. 170 174. 80. J. Kong, F. Koushanfar, P. K. Pendyala, A.-R. Sadeghi, and C. Wachsmann, PUFatt: Embedded Platform Attestation based on Novel Processor-based PUFs, in Design Automation Conference (DAC) 2014, Best paper candidate, Jun 2014, pp. 1 6. 79. M. Rostami, J. B. Wendt, M. Potkonjak, and F. Koushanfar, Quo Vadis, PUF? Trends and Challenges of Emerging Physical-Disorder based Security, in Design, Automation & Test in Europe (DATE), Mar 2014, pp. 1 6. 78. A. Munir and F. Koushanfar, D2Cyber: A Design Automation Tool for Dependable Cybercars, in Design, Automation & Test in Europe (DATE), Mar 2014, pp. 1 4. 77. J. B. Wendt, F. Koushanfar, and M. Potkonjak, Techniques for Foundry Identification, in Design Automation Conference (DAC), Jun 2014, pp. 1 6. 76. A. Munir, F. Koushanfar, H. Seudié, and A.-R. Sadeghi, Cycar 2013: First international academic workshop on security, privacy and dependability for cybervehicles, in ACM Conference on Computer & Communications Security (CCS), Nov 2013, pp. 1481 1482. 75. M. Rostami, F. Koushanfar, J. Rajendran, and R. Karri, Hardware Security: Threat Models and Metrics, in International Conference on Computer-Aided Design (ICCAD), Nov 2013, pp. 819 823. 74. M. Rostami, A. Juels, and F. Koushanfar, Heart-to-Heart (H2H): Authentication for Implanted Medical Devices, in ACM Conference on Computer & Communications Security (CCS), Nov 2013, pp. 1099 1112. 73. Y. Yao, M.-B. Kim, J. Li, I. L. Markov, and F. Koushanfar, ClockPUF: Physical Unclonable Functions based on Clock Networks, in Design, Automation & Test in Europe (DATE), Mar 2013, pp. 422 427. 72. K. Hu, A. N. Nowroz, S. Reda, and F. Koushanfar, High-sensitivity Hardware Trojan Detection using Multimodal Characterization, in Design, Automation & Test in Europe (DATE), Mar 2013, pp. 1271 1276. 71. A. Mirhoseini, E. M. Songhori, and F. Koushanfar, Idetic: A High-level Synthesis Approach for enabling Long Computations on Transiently-powered ASICs, in Pervasive Computing and Communication conference (PerCom), Mar 2013, pp. 216 224. 70. A. Mirhoseini, E. M. Songhori, and F. Koushanfar, Automated Checkpointing for Enabling Intensive Applications on Energy Harvesting Devices, in International Symposium on Low Power Electronics and Design (ISLPED), Sep 2013, pp. 27 32. 69. M. Rostami, W. Burleson, A. Juels, and F. Koushanfar, Balancing Security and Utility in Medical Devices? in Design Automation Conference (DAC), Jun 2013, pp. 1 6. 68. S. Wei, K. Li, F. Koushanfar, and M. Potkonjak, Provably Complete Hardware Trojan Detection using Test Point Insertion, in International Conference on Computer-Aided Design (ICCAD), Nov 2012, pp. 51 63. 67. F. Koushanfar, S. Fazzari, C. McCants, W. Bryson, M. Sale, P. Song, and M. Potkonjak, Can EDA Combat the rise of Electronic Counterfeiting? in Design Automation Conference (DAC), Jun 2012, pp. 133 138. 66. A. Mirhoseini, M. Potkonjak, and F. Koushanfar, Coding-based Energy Minimization for Phase Change Memory, in Design Automation Conference (DAC), Jun 2012, pp. 68 76. 65. F. Koushanfar, A.-R. Sadeghi, and H. Seudie, EDA for Secure and Dependable Cybercars: Challenges and Opportunities, in Design Automation Conference (DAC), Jun 2012, pp. 220 228. 64. S. Wei, K. Li, F. Koushanfar, and M. Potkonjak, Hardware Trojan Horse Benchmark via Optimal Creation and Placement of Malicious Circuitry, in Design Automation Conference (DAC), Jun 2012, pp. 90 95. 63. M. Majzoobi, M. Rostami, F. Koushanfar, D. S. Wallach, and S. Devadas, Slender PUF Protocol: A Lightweight, Robust, and Secure Authentication by Substring Matching, in International Workshop on Trustworthy Embedded Devices, May 2012, pp. 33 44.

62. F. Koushanfar and A. Mirhoseini, Hybrid Heterogeneous Energy Supply Networks, in IEEE International Symposium on Circuits and Systems (ISCAS), May 2011, pp. 2489 2492. 61. E. Dyer, M. Majzoobi, and F. Koushanfar, Hybrid Modeling of Non-stationary Process Variations, in Design Automation Conference (DAC), Jun 2011, pp. 194 199. 60. A. Mirhoseini and F. Koushanfar, Hypoenergy: Hybrid Supercapacitor-battery Power-supply Optimization for Energy Efficiency, in Design, Automation & Test in Europe (DATE), Mar 2011, pp. 887 890. 59. S. Wei, F. Koushanfar, and M. Potkonjak, Integrated Circuit Digital Rights Management Techniques using Physical Level Characterization, in ACM workshop on Digital rights management (DRM), Oct 2011, pp. 3 14. 58. F. Koushanfar, Integrated Circuits Metering for Piracy Protection and Digital Rights Management: An Overview, in GLSVLSI, May 2011, Invited Paper, pp. 449 454. 57. A. Mirhoseini and F. Koushanfar, Learning to Manage Combined Energy Supply Systems, in International Symposium on Low Power Electronics and Design (ISLPED), Aug 2011, pp. 229 234. 56. M. Majzoobi, G. Ghiaasi-Hafezi, F. Koushanfar, and S. Nassif, Ultra-low Power Current-based PUF, in IEEE International Symposium on Circuits and Systems (ISCAS), May 2011, pp. 2071 2074. 55. M. Majzoobi, F. Koushanfar, and S. Devadas, FPGA PUF using Programmable Delay Lines, in IEEE Workshop on Information Forensics and Security, Dec 2010, pp. 1 6. 54. F. Koushanfar, Hierarchical Hybrid Power Supply Networks, in Design Automation Conference (DAC), Jun 2010, pp. 629 630. 53. F. Koushanfar and Y. Alkabani, Provably Secure Obfuscation of Diverse Watermarks for Sequential Circuits, in International Symposium on Hardware-Oriented Security and Trust (HOST), Jun 2010, pp. 42 47. 52. M. Majzoobi, E. Dyer, A. Elnably, and F. Koushanfar, Rapid FPGA Characterization using Clock Synthesis and Signal Sparsity, in International Test Conference (ITC), Nov 2010, pp. 457 466. 51. A. Mirhoseini, Y. Alkabani, and F. Koushanfar, Real Time Emulations: Foundation and Applications, in Design Automation Conference (DAC), Jun 2010, p. 623 624. 50. Y. Alkabani and F. Koushanfar, Consistency-based Characterization for IC Trojan Detection, in International Conference on Computer Aided Design (ICCAD), Nov 2009, pp. 123 127. 49. F. Koushanfar and D. Shamsi, The Challenges of Model Objective Selection and Estimation for Ad-hoc Network Data Sets, Institute of Mathematical Statistics (IMS) Lecture Notes-Monograph Series (LNMS), vol. 57, pp. 332 345, 2009. 48. Y. Alkabani, F. Koushanfar, and M. Potkonjak, N-version Temperature-aware Scheduling and Binding, in International Symposium on Low Power Electronics and Designs (ISLPED), 2009, pp. 331 334. 47. A. Candore, O. Kocabas, and F. Koushanfar, Robust Stable Radiometric Fingerprinting for Wireless Devices, in IEEE International Workshop on Hardware-Oriented Security and Trust, 2009, pp. 43 49. 46. Y. Alkabani, F. Koushanfar, N. Kiyavash, and M. Potkonjak, Trusted Integrated Circuits: A Nondestructive Hidden Characteristics Extraction Approach, in Information Hiding (IH), 2008, pp. 102 117. 45. M. Potkonjak and F. Koushanfar, (Bio)-behavioral CAD, in Design Automation Conference (DAC), 2008, pp. 351 352. 44. Y. Alkabani and F. Koushanfar, Active Control and Digital Rights Management of Integrated Circuit IP Cores, in Compilers, Architectures, and Synthesis for Embedded Systems (CASES), 2008. 43. J. Roy, F. Koushanfar, and I. Markov, Epic: Ending Piracy of Integrated Circuits, in Design Automation & Test in Europe (DATE), 2008. 42. Y. Alkabani, T. Massey, F. Koushanfar, and M. Potkonjak, Input Vector Control for Post-Silicon Leakage Current Minimization in the Presence of Manufacturing Variability, in Design Automation Conference (DAC), Jun 2008, pp. 606 609. 41. M. Majzoobi, F. Koushanfar, and M. Potkonjak, Lightweight Secure PUFs, in International Conference on Computer-Aided Design (ICCAD), 2008, pp. 670 673.

40. Y. Alkabani and F. Koushanfar, N-variant IC Design: Methodology and Applications, in Design Automation Conference (DAC), 2008, pp. 546 551. 39. D. Shamsi, P. Boufounos, and F. Koushanfar, Noninvasive Leakage Power Tomography of Integrated Circuits by Compressive Sensing, in International symposium on Low power electronics and design (ISLPED), 2008, pp. 341 346. 38. F. Koushanfar, P. Boufounos, and D. Shamsi, Post-silicon Timing Characterization by Compressed Sensing, in International Conference on Computer Aided Design (ICCAD), 2008, pp. 185 189. 37. J. Roy, F. Koushanfar, and I. Markov, Protecting Bus-based Hardware IP by Secret Sharing, in Design Automation Conference (DAC), 2008, pp. 846 851. 36. M. Majzoobi, F. Koushanfar, and M. Potkonjak, Testing Techniques for Hardware Security, in International Test Conference (ITC), 2008, pp. 1 10. 35. D. Shamsi, F. Koushanfar, and M. Potkonjak, Challenging Benchmark for Location Discovery in Ad-hoc Networks, in ACM international symposium on Mobile ad hoc networking and computing (MobiHoc), 2008, p. 361. 34. J. A. Roy, F. Koushanfar, and I. L. Markov, Extended Abstract: Circuit CAD Tools as a Security Threat, in IEEE International Workshop on Hardware-Oriented Security and Trust (HOST), 2008, pp. 65 66. 33. Y. Alkabani and F. Koushanfar, Extended Abstract: Designer s Hardware Trojan Horse, in IEEE International Workshop on Hardware-Oriented Security and Trust (HOST), 2008, pp. 82 83. 32. N. Kiyavash and F. Koushanfar, Anti-collusion Position Estimation in Wireless Sensor Networks, in IEEE Mobile Ad-hoc and Sensor Systems (MASS), 2007. 31. F. Koushanfar and M. Potkonjak, CAD-based Security, Cryptography, and Digital Rights Management, in Design Automation Conference (DAC), 2007. 30. F. Koushanfar and M. Potkonjak, A General Framework for Spatial Correlation Modeling in VLSI Design, in Design Automation Conference (DAC), 2007, pp. 268 269. 29. F. Koushanfar and M. Potkonjak, Hardware Security: Preparing Students for the Next Design Frontiers, in IEEE International Conference on Microelectronic Systems Education, 2007. 28. F. Koushanfar, M. Potkonjak, Integration of Statistical Techniques in the Design Curriculum, in IEEE International Conference on Microelectronic Systems Education, 2007, pp. 153-154. 27. S. So, F. Koushanfar, A. Kosterev, and F. Tittel, Laserspecks: Laser Spectroscopic Trace-gas Sensor Networks - Sensor Integration and Applications, in Information Processing in Sensor Networks (IPSN), 2007, pp. 226 235. 26. Y. Alkabani, F. Koushanfar, and M. Potkonjak, Remote Activation of ICs for Piracy Prevention and Digital Right Management, in International Conference on Computer Aided Design (ICCAD), 2007, pp. 674 677. 25. F. Koushanfar, A. Daware, D. Nguyen, M. Potkonjak, and A. Sangiovanni-Vincentelli, Techniques for Maintaining Connectivity Ad-hoc Networks under Energy Constraints, ACM Transaction on Embedded Computing Systems, vol. 6, 2007, pp. 16 16. 24. Y. Alkabani and F. Koushanfar, Active Hardware Metering for Intellectual Property Protection and Security, in USENIX Security Symposium, 2007, pp. 291 306. 23. F. Koushanfar and M. Potkonjak, Watermarking Technique for Sensor Networks: Foundations and Applications. CRC Press, 2006. 22. F. Koushanfar, N. Kiyavash, and M. Potkonjak, Interacting Particle-based Model for Missing Data in Sensor Networks: Foundations and Applications, in IEEE Sensors, 2006, pp. 888 891. 21. F. Koushanfar, N. Taft, and M. Potkonjak, Sleeping Coordination for Comprehensive Sensing using Isotonic Regression and Domatic Partitions, in IEEE International Conference on Computer Communications (INFO- COM), 2006, pp. 1 13. 20. J. Wong, F. Koushanfar, and M. Potkonjak, Flexible ASICs: Shared Masking for Multiple Media Processors, in Design Automation Conference (DAC), 2005, pp. 909 914.

19. F. Koushanfar and M. Potkonjak, Markov Chain-based Models for Missing and Faulty Data in Mica2 Sensor Motes, in IEEE Sensors, 2005, pp. 1430 1434. 18. F. Koushanfar, M. Potkonjak, and A. Sangiovanni-Vincentelli, Error Models for Light Sensors by Statistical Analysis of Raw Sensor Measurements, in IEEE Sensors, vol. 2, Oct 2004, pp. 1472 1475. 17. F. Koushanfar, A. Davare, D. Nguyen, M. Potkonjak, and A. Sangiovanni-Vincentelli, Low Power Coordination in Wireless Ad-hoc Networks, in International Symposium on Low Power Electronics and Design (ISLPED), 2003, pp. 475 480. 16. F. Koushanfar, M. Potkonjak, and A. Sangiovanni-Vincentelli, On-line Fault Detection of Sensor Measurements, in IEEE Sensors, vol. 2, Oct 2003, pp. 974 979. 15. F. Koushanfar, S. Slijepcevic, M. Potkonjak, and A. Sangiovanni-Vincentelli, Error-tolerant Multi-modal Sensor Fusion, in IEEE CAS Workshop on Wireless Communications and Networking, 2002. 14. F. Koushanfar, J. Wong, J. Feng, and M. Potkonjak, Ilp-based Engineering Change, in Design Automation Conference (DAC), 2002, pp. 910 915. 13. J. Feng, F. Koushanfar, and M. Potkonjak, System-architectures for Sensor Networks Issues, Alternatives, and Directions, in International Conference of Computer Design, 2002, pp. 226 231. 12. F. Koushanfar, S. Slijepcevic, J. Wong, and M. Potkonjak, Global Error-tolerant Algorithms for Location Discovery in Ad-hoc Wireless Networks, in IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2002, pp. IV 4186 IV 4186. 11. F. Koushanfar, M. Potkonjak, and A. Sangiovanni-Vincentelli, Fault Tolerance Techniques for Wireless Adhoc Sensor Networks, in IEEE Sensors, vol. 2, 2002, pp. 1491 1496. 10. S. Meguerdichian, F. Koushanfar, G. Qu, and M. Potkonjak, Exposure in Wireless Ad-hoc Sensor Networks, in International conference on Mobile computing and networking (MobiCom), 2001, pp. 139 150. 9. F. Koushanfar and G. Qu, Hardware Metering, in Design Automation Conference (DAC), 2001, pp. 490 493. 8. F. Koushanfar, G. Qu, and M. Potkonjak, Intellectual property metering, in Information Hiding Workshop (IHW), 2001, pp. 81 95. 7. J. Wong, F. Koushanfar, S. Meguerdichian, and M. Potkonjak, A Probabilistic Constructive Approach to Optimization Problems, in International Conference on Computer Aided Design (ICCAD), 2001, pp. 453 456. 6. S. Meguerdichian, F. Koushanfar, A. Morge, D. Petranovic, and M. Potkonjak, Metacores: Design and Optimization Techniques, in Design Automation Conference (DAC), 2001, pp. 585 590. 5. S. Meguerdichian, F. Koushanfar, M. Potkonjak, and M. Srivastava, Coverage Problems in Wireless Ad-hoc Sensor Networks, in IEEE International Conference on Computer Communications (INFOCOM), vol. 3, 2001, pp. 1380 1387. 4. J. Rabaey, M. Potkonjak, F. Koushanfar, S. Li, and T. Tuan, Challenges and Opportunities in Broadband and Wireless Communication Designs, in International Conference on Computer Aided Design (ICCAD), 2000, pp. 76 82. 3. F. Koushanfar, V. Prabhu, M. Potkonjak, and J. Rabaey, Processors for Mobile Applications, in International Conference on Computer Designs (ICCD), 2000, pp. 603 608. 2. F. Koushanfar, D. Kirovski, and M. Potkonjak, Symbolic Debugging Scheme for Optimized Hardware and Software, in International Conference on Computer Aided Design (ICCAD), 2000, pp. 40 43. 1. A. E. Caldwell, Y. Cao, A. B. Kahng, F. Koushanfar, H. Lu, I. L. Markov, M. Oliver, D. Stroobandt, and D. Sylvester, GTX: The Marco GSRC Technology Extrapolation System, in Design Automation Conference (DAC), 2000, pp. 693 698. Book Chapters

10. U. Rührmair, X. Xu, J. Sölter, A. Mahmoud, M. Majzoobi, F. Koushanfar, and W. Burleson, Efficient Power and Timing Side Channels for Physical Unclonable Functions, ser. Lecture Notes in Computer ScienceCryptographic Hardware and Embedded Systems CHES 2014, 2014, vol. 8731. 9. M. Majzoobi, F. Koushanfar, and S. Devadas, FPGA-based True Random Number Generation using Circuit Metastability with Adaptive Feedback Control, 2011, pp. 17 32. 8. M. Majzoobi, F. Koushanfar, and M. Potkonjak, FPGA-oriented Security, 2011. 7. F. Koushanfar, Hardware Metering: A Survey., 2011. 6. U. Ruhrmair, S. Devadas, and F. Koushanfar, Security based on Physical Unclonability and Disorder, Springer 2011. 5. M. Majzoobi, A. Elnably, and F. Koushanfar, FPGA Time-bounded Unclonable Authentication, in Information Hiding, ser. Lecture Notes in Computer Science, vol. 6387, 2010, pp. 1 16. 4. F. Koushanfar, A. Mirhoseini, and Y. Alkabani, A Unified Framework for Multimodal Submodular IC Trojan Detection, in Information Hiding Conference, 2010, pp. 17 32. 3. M. Nelson, A. Nahapetian, F. Koushanfar, and M. Potkonjak, SVD-based Ghost Circuitry Detection, in Information Hiding (IH), 2009, pp. 221 234. 2. F. Koushanfar, M. Potkonjak, and J. Feng, Sensor Network Architecture, 2005. 1. F. Koushanfar, S. Slijepcevic, M. Potkonjak, and A. Sangiovanni-Vincentelli, Location Discovery in Ad-hoc Wireless Sensor Networks, 2003. Patents 11. J. Roy, F. Koushanfar, I. Markov, Methods for Protecting Against Piracy of Integrated Circuits., US Patent application 20100284539, Filed Mar 9, 2010. 10. F. Koushanfar, M. Potkonjak, Methods and systems of digital rights management for integrated circuits, US Patent application 20100122353 A1, Filed Aug 7, 2009. 9. J. Roy, F. Koushanfar, I. Markov, Protecting Bus-based Hardware IP by Secret Sharing., US Patent 8732468 B2, Issued May 20, 2014. 8. M. Potkonjak, F. Koushanfar, Controlling integrated circuits including remote activation or deactivation, US Patent 8387071 B2, Issued Feb 26, 2013. 7. M. Potkonjak, F. Koushanfar, Identification of integrated circuits, US Patent 8620982 B2, Issued Dec 31, 2013. 6. F. Koushanfar, M. Potkonjak, Hardware synthesis using thermally aware scheduling and binding, US Patent 8656338 B2, Issued Feb 14, 2014. 5. M. Potkonjak, F. Koushanfar, Identification of integrated circuits, US Patent 8788559 B2, Issued Jul 22, 2014. 4. F. Koushanfar, M. Potkonjak, Lightweight secure physically unclonable functions, US Patent 7898283 B1, Issued Mar 1, 2011. 3. F. Koushanfar, Method for N-variant integrated circuit (IC) design, and IC having N-variant circuits implemented therein, US Patent 8176448 B2, Issued May 8, 2012. 2. F. Koushanfar, M. Potkonjak, Testing security of mapping functions, US Patent 8370787 B2, Issued Feb 5, 2013. 1. F. Koushanfar, M. Potkonjak, Input vector selection for reducing current leakage in integrated circuits, US Patent 8443034 B2, Issued May 14, 2013. Thesis

3. F. Koushanfar. Ensuring Data Integrity in Sensor-based Networked Systems. Ph.D. dissertation, Electrical Engineering and Computer Science (EECS) Department, University of California, Berkeley, Dec 2005. Advisor: Prof. Alberto Sangiovanni-Vincentelli, Prof. Miodrag Potkonjak. 2. F. Koushanfar. Statistical Modeling and Recovery of Intermittent Sensor Data Streams. M.A. thesis, Statistics Department, University of California, Berkeley, May 2005. Advisor: Prof. David Brillinger. 1. F. Koushanfar. Iterative Error-tolerant Location Discovery in Ad-hoc Wireless Sensor Networks. M.S. thesis, Electrical Engineering (thesis joint in Computer Science), University of California, Los Angeles, 2001. Advisors: Prof. Miodrag Potkonjak, Prof. Mani Srivastava. Professional Activities Keynote Speaker (Invited) IEEE Symposium on Privacy-Aware Computing (IEEE PAC), 2017 IEEE Workshop for Women in Hardware and Systems Security (WISE), visionary talk, 2017. ACM Workshop on Scalable Trusted Computing (ACM STC), co-located with ACM Conference on Computer and Communications Security (CCS), 2011 Associate Editor IEEE Transactions on Information Forensics and Security, 2012-present IEEE Transactions on Very Large Scale Integration Systems (VLSI), 2009-2011 Ad-Hoc Networks Journal (Elsevier), 2010-2014 Guest Editor ACM Transactions on Design Automation of Electronic Systems (TODAES), 2017 IEEE Transactions on Computer-Aided Design (TCAD), Hardware Security and Trust, 2015 Proceedings of IEEE, Special Issue on Hardware Security, 2014 IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), Special Issue on Low-Power, Reliable, and Secure Solutions for Realization of Internet of Things, 2012 IEEE Design and Test, Special Issue on Verifying Physical Trustworthiness of Integrated Circuits and Systems, January 2010 General Chair IEEE CANDE (Computer-Aided Network DEsign) Committee, 2012-present IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), 2014 ACM CCS CyCar (Workshop on Security, Privacy and Dependability for Cyber Vehicles), 2013 Program Chair, Treasurer, Secretary IEEE CANDE (Computer-Aided Network DEsign) Committee, 2009-2011 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), 2012-13 Steering Committee Member, Industrial Liaison, and publicity chair IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), 2009-2011 IEEE Society Activities IEEE Signal Processing Society (SP) member of the Information Forensics and Security Technical Committee, 2015-Present IEEE Circuits and Systems Society (CASS) representative for IEEE SYSC Technical Committee on Security and Privacy in Complex Information Systems, 2011-2013

Technical Program Committee (TPC) Memberships USENIX Security Symposium, 2014-2016 ACM/IEEE Design Automation Conference (DAC), security track chair, 2014-2016 Internet Society Network and Distributed System Security Symposium (NDSS), 2016-2017 IEEE Symposium on Security and Privacy (Oakland, S&P), 2009, 2014-2017 Workshop on Cryptographic Hardware and Embedded Systems (CHES), 2014 ACM Conference on Computer and Communications Security (CCS), 2013 ACM/IEEE Design Automation Conference (DAC), 2009-2011 IEEE Conference on Computer Communications (INFOCOM), 2007, 2009-2012 Workshop on Special Aspects of Cyber Physical Systems: Trustworthy Embedded Devices (TRUSTED), 2011 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2010 IEEE Workshop on Hardware-Oriented Security and Trust (HOST), 2008-2010 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2007 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), 2008 Great Lakes Symposium on VLSI (GLVLSI), 2007-2009, 2016 IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2007 IEEE Statistical Signal Processing Workshop (SSP), 2007 IEEE/ACM International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), 2007 Reviewer for: ACM Transactions on Reconfigurable Technology and Systems (TRETS) ACM Transactions on Embedded Computing Systems (TECS) ACM Transactions on Information and System Security (TISSEC) ACM Transactions on Design Automation of Electronic Systems (TODAES) IEEE Transactions on Computer Aided Design (TCAD) IEEE IEEE Transactions on Emerging Topics in Computing (TETC) IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) IEEE Transactions on Dependable and Secure Computing (TDSC) IEEE Transactions on Mobile Computing (TMC) ACM Transactions on Sensor Networks (TOSN) IEEE Transactions on Wireless Communications IEEE/ACM Transactions on Networking (TON) Technical Advising Expert Technical Advisor to Judge Ron Clark, United States District Court, 2009-2010 Invited talks: Invited colloquium/seminar speaker at many academic institutions, including Boston U, Caltech, CMU, MIT, Purdue, Stanford, TU Darmstadt, UC Berkeley, UC Los Angeles, UC San Diego, U of Illinois Urbana-Champaign, U of Minnesota, U of Washington Seattle, and USC Invited speaker at major semiconductor design, research and development companies including IBM, TI and Intel, at several army workshops organized by DARPA, ARO, DoD, and Homeland Security, and at the National Security Agency (NSA)

Other: Academic advisor, ARO Strategy Advisory Meeting, 2014 Founder and faculty advisor, Women ExCEL (Electrical & Computer Engineering Leaders), 2008-Present Co-Founder and Co-PI, NSF Trust-Hub Community Research Initiative for Hardware Security and Trust, 2011-Present Panel member, NSF Directorate for Computer & Information Science (CISE), multiple times 2007, 2008, 2009, 2011, 2015 Panel member, NSF Advance workshop for women in science and engineering, Rice University, 2006-2009

Kenneth Kreutz-Delgado Department of Computer Electrical and Computer Engineering University of California, San Diego 9500 Gilman Drive, MC 0407 La Jolla, CA 92093-0407 Phone: 858-922-0028 www.ece.ucsd.edu Professional Preparation UC San Diego, La Jolla CA Engineering Systems Science PhD 1985 UC San Diego, La Jolla CA Engineering Systems Science MS 1982 UC San Diego, La Jolla CA Physics MS 1978 UC San Diego, La Jolla CA Physics BA 1976 Professional & Academic Appointments 1998-Now Professor, ECE Department, University of California, San Diego 1998-1999 Visiting Research Scientist, Computational Neurobiology Laboratory, Salk Institute 1995-1998 Associate Professor, ECE Department, University of California, San Diego 1991-1995 Assistant Professor, ECE Department, University of California, San Diego 1989-1991 Assistant Professor, AMES (MAE) Department, University of California, San Diego 1989-1990 Visiting Associate Researcher, California Institute of Technology 1985-1989 Machine Intelligence Systems Group, NASA/Jet Propulsion Laboratory 1978-1980 Scientific Programmer, Neurosciences Department, University of California, San Diego Related Publications [1] S. Das, B. Pedroni, P. Merolla, J. Arthur, A.S. Cassidy, D. Modha, G. Cauwenberghs, K. Kreutz- Delgado, Gibbs Sampling with Low-Power Spiking Digital Neurons," IEEE International Symposium on Circuits & Systems (ISCAS), 2015. [2] E. Neftci, S. Das, B. Pedroni, K. Kreutz-Delgado, G. Cauwenberghs, Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems," Frontiers in Neuroscience, 7, 2013. [3] B. Pedroni, S. Das, E. Neftci, K. Kreutz-Delgado, G. Cauwenberghs, Neuromorphic Adaptations of Restricted Boltzmann Machines and Deep Belief Networks," International Joint Conference on Neural Networks (IJCNN), 2013. [4] S. Makeig, C. Kothe, T. Mullen, N. Bigdely-Shamlo, Z. Zhang, K. Kreutz-Delgado, Evolving Signal Processing for Brain-Computer Interfaces," Proceedings of the IEEE, Vol. 100 (Special Centennial Issue), 1567-1584, 2013. [5] J.F. Murray, K. Kreutz-Delgado, Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning," Neural Computation, 19, 2301-2352, 2007. Other Significant Publications [6] F.D. Broccard, T. Mullen, Y.M. Chi, D. Peterson, J.R. Iversen, M. Arnold, K. Kreutz-Delgado, T.- P. Jung, S. Makeig, H. Poizner, T. Sejnowski, G. Cauwenberghs, Closed-Loop Brain-Machine- Body Interfaces for Noninvasive Rehabilitation of Movement Disorders," Annals of Biomedical Engineering, 42(8), 1-21, 2014. [7] N. Bigdely-Shamlo, T. Mullen, K. Kreutz-Delgado, S. Makeig, Measure Projection Analysis: a Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference," Neuroimage, 72, 287-303, 2013. [8] S.F. Cotter, B.D. Rao, K. Engan, K. Kreutz-Delgado, Sparse Solutions to Linear Inverse Problems with Multiple Measurement Vectors," IEEE Transactions on Signal Processing, 7, 2477-88, 2005. 1

[9] J.A. Palmer, K. Kreutz-Delgado, B.D. Rao, D.P. Wipf, Variational EM Algorithms for Non- Gaussian Latent Variable Models," Neural Information Processing Systems (NIPS), 18, 2005. [10] J.F. Murray, G. F Hughes, K. Kreutz-Delgado, Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application," Journal of Machine Learning Research, 6, 2005. Synergistic Activities Award Recognitions Fellow of the IEEE (Signal Processing Society); NASA Award of Recognition for Creative Development of an Exceptional Technical Contribution; NASA Group Achievement Award: Telerobotics Technology Team; NSF Presidential Young Investigator Award Professional Activities Former Senior Area Editor, IEEE Signal Processing Letters (2012-2015); Former Area Editor for IEEE Transactions on Signal Processing; Former Area Editor for IEEE Transactions on Robotics & Automation. Non-Departmental Academic Affiliations Founding Director of the newly established UCSD Calit2/QI Pattern Recognition Institute (PRLab); Member UCSD Institute for Neural Computation (INC); Member UCSD Swartz Center for Computational Neuroscience (SCCN); Member UCSD Contextual Robotics Institute; Member Cymer Conference Organization Local Arrangements Chair for 2014 International Conference on Systems, Man & Cybernetics Memberships in Professional Organizations Member of AAAS. IEEE Society Memberships: Computer; Signal Processing; Robotics &Automation; Information Theory; Engineering in Medicine & Biology; SMC; Control Systems 2

Gert Lanckriet Biographical Sketch Gert Lanckriet leads Machine Learning at Amazon Music, and is a Professor of Electrical and Computer Engineering at UC San Diego. His interests are in data science, on the interplay between machine learning, applied statistics, and large-scale optimization, with applications to music and video search and recommendation, multimedia, and personalized, mobile health. He was awarded the SIAM Optimization Prize in 2008 and is the recipient of a Hellman Fellowship, an IBM Faculty Award, an NSF CAREER Award, and an Alfred P. Sloan Foundation Research Fellowship. In 2011, MIT Technology Review named him one of the 35 top young technology innovators in the world (TR35). In 2014, he received the Best Ten-Year Paper Award at the International Conference on Machine Learning. He co-founded Keevio, Inc., a content-based music, sound, and video analytics company, and Benefunder, an innovative organization that works with wealth management firms to connect philanthropists with leading researchers across the nation to fund their research. He received a master s degree in electrical engineering from the Katholieke Universiteit Leuven, Belgium, and M.S. and Ph.D. degrees in electrical engineering and computer science from UC Berkeley.

BILL LIN Biographical Sketch EDUCATION Ph.D, Electrical Engineering and Computer Sciences University of California, Berkeley, May 1991 Masters of Science, Electrical Engineering and Computer Sciences University of California, Berkeley, May 1988 Bachelor of Science, Electrical Engineering and Computer Sciences University of California, Berkeley, May 1985 PROFESSIONAL EXPERIENCE 1997 present University of California, San Diego Professor, Electrical and Computer Engineering Adjunct Professor, Computer Science and Engineering 1992 1996 IMEC, Leuven, Belgium Research Group Head, Systems Control and Communications Group HONORS AND AWARDS Keynote Speaker, IEEE International Performance, Computing and Communications Conference (2013) Keynote Speaker, IEEE Green Computing Conference, Lighter-than-Green Dependable Multicore workshop (2012) Best Paper Award Nomination, ACM/IEEE Design Automation Conference (2012) Hellman Fellowship (1998-1999) Best Paper Award Nomination, Design Automation and Test in Europe Conference (1998) Best Paper Award, IEEE Transactions on VLSI Systems (1995) Best Paper Award Nomination, ACM/IEEE Design Automation Conference (1994) Distinguished Paper Citation, IEEE International Conference on Computer-Aided Design (1990) Distinguished Paper Citation, IFIP International Conference on VLSI (1989) Best Paper Award, ACM/IEEE Design Automation Conference (1987) PROFESSIONAL ACTIVITIES Served or serving as Editor on 3 ACM or IEEE journals, as General Chair on 4 ACM or IEEE conferences, on the Organizing or Steering Committees for 5 ACM or IEEE conferences, and on the Technical Program Committees of 44 ACM or IEEE conferences. SELECTED PROFESSIONAL ACTIVITIES General Chair, ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS), 2010 Chair of Steering Committee, ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS), 2010-present (Chair since 2012) Guest Editor, IEEE Transactions on Network and Service Management (TNSM), 2012-2013 General Co-Chair, ACM SIGMETRICS, 2015 General Chair, ACM/IEEE International Symposium on Quality-of-Service (IWQoS), 2011

Steering Committee, ACM/IEEE International Symposium on Quality-of-Service (IWQoS), 2011-present Associate Editor, ACM Transactions on Design Automation and Electronics Systems (TODAES), 2010-2015 Editorial Board, International Journal of Embedded Systems, 2003-present General Chair, ACM/IEEE Symposium on Networks-on-Chips (NOCS), 2009 PUBLICATIONS Published over 170 journal articles and conference papers in top-tier venues and publications. SELECTED PUBLICATIONS Sen Yang, Bill Lin, Jun Xu, Safe Randomized Load-Balanced Switching by Diffusing Extra Load, ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science, Irvine, CA, June 18-22, 2018. Sen Yang, Bill Lin, Paul Tune, Jun Xu, A Simple Re-Sequencing Load-Balanced Switch based on Analytical Packet Reordering Bounds, IEEE Conference on Computer Communications (INFOCOM), Atlanta, GA, May 1-4, 2017. Yang Song, Kambiz Samadi, Bill Lin, A Single-Tier Virtual Queuing Memory Controller Architecture for Heterogeneous MPSoCs, ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 22, no. 3, May 2017. Chia-Wei Chang, Guanyao Huang, Bill Lin, Chen-Nee Chuah, LEISURE: Load-Balanced Network-Wide Traffic Measurement and Monitor Placement, IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 4 (April 2015), pp. 1059-1070. Xiaodong Yu, Bill Lin, Michela Becchi, Revisiting State Blow-Up: Automatically Building Augmented-FA While Preserving Functional Equivalence, IEEE Journal on Selected Areas in Communications, 32(10): 1822-1833 (2014). Hao Wang, Bill Lin, Reservation-Based Packet Buffers with Deterministic Packet Departures, IEEE Transactions Parallel Distributed Systems, 25(5): 1297-1305 (2014). Weijun Ding, Jun (Jim) Xu, Jim Dai, Yang Song, Bill Lin, Sprinklers: A Randomized Variable-Size Striping Approach to Reordering-Free Load-Balanced Switching, ACM CoNEXT 2014. Rohit Sunkam Ramanujam, Bill Lin, Destination-based Congestion Awareness for Adaptive Routing in 2D Mesh Networks, ACM Transactions on Design Automation of Electronics Systems, vol. 18, no. 4, (October 2013), pp. 60:1-60:27. Hao Wang, Haiquan (Chuck) Zhao, Bill Lin, and Jun (Jim) Xu, Robust Pipelined Memory System with Worst Case Performance Guarantee, IEEE Transactions on Computers, October 2012. Guanyao Huang, Chia-Wei Chang, Chen-Nee Chuah and Bill Lin, Measurement-Aware Monitor Placement and Routing: A Joint Optimization Approach for Network-Wide Measurements, IEEE Transactions on Network and Service Management, vol. 9, no. 1, March 2012, pp. 48-59. Hao Wang, Haiquan Zhao, Bill Lin, and Jun Xu, DRAM-based statistics counter array architecture with performance guarantee. IEEE/ACM Transactions on Networking, vol. 20, no. 4 (2012): 1040-1053. Rohit Sunkam Ramanujam, Bill Lin, Randomized Throughput-Optimal Oblivious Routing for Torus Networks, IEEE Transactions on Computers, IEEE Digital Library, publication date: November 29, 2011, Rohit Sunkam Ramanujam, Bill Lin, Randomized Partially-minimal Routing: Near-Optimal Oblivious Routing for 3D Mesh Networks, IEEE Transactions on VLSI, IEEE Digital Library, publication date: November 24, 2011. Nan Hua, Bill Lin, Jun Xu, Haiquan Zhao, BRICK: A Novel Exact Active Statistics Counter Architecture, IEEE/ACM Transactions on Networking, vol. 19, no. 3, June 2011, pp. 670-682.

Rohit Sunkam Ramanujam, Vassos Soteriou, Bill Lin, Li-Shiuan Peh, Extending the Effective Throughput of NoCs with Distributed Shared-Buffer Routers, IEEE Transactions on Computer-Aided Design of Circuits and Systems, vol. 30, No. 4 (April 2011), pp. 548-561. Chia-Wei Chang, Seungjoon Lee, Bill Lin, Jia Wang, The Taming of the Shrew: Mitigating Low-Rate TCP Targeted Attack, IEEE Transactions on Network and Service Management, 7(1): 1-13 (2010). Bill Lin, Isaac Keslassy, The Concurrent Matching Switch Architecture, IEEE/ACM Transactions on Networking, vol. 18, no. 4, August 2010, pp. 1330-1343. Bill Lin, Isaac Keslassy, The Interleaved Matching Switch Architecture, IEEE Transactions on Communications, vol. 57, no. 12, December 2009. Jerry C. Chou, Bill Lin, Subhabrata Sen, Oliver Spatscheck, Proactive Surge Protection: A Defense Mechanism for Bandwidth-Based Attacks, IEEE/ACM Transactions on Networking, 17(6): 1711-1723 (2009). Jerry Chou, Bill Lin, Coarse Circuit Switching by Default, Re-Routing Over Circuits for Adaptation, Journal of Optical Networking, vol. 8, no. 1, pp. 33-50, January 2009.

Siavash Mirarab Contact Information Academic Positions Department of Electrical and Computer Engineering phone: 858 822 6245 UC San Diego e-mail: smirarab@ucsd.edu 9500 Gilman Drive, Mail code 0407 http://eceweb.ucsd.edu/~smirarab/ La Jolla, CA 92093-0407 google scholar: http://goo.gl/geswq5 UC San Diego, San Diego, CA, United States Department of Electrical and Computer Engineering Assistant Professor July 2015 present Education Ph.D Jan. 2011 July 2015 University of Texas at Austin, Austin, Texas, Department of Computer Science Advisors: Prof. Tandy Warnow, Prof. Keshav Pingali Novel scalable approaches for multiple sequence alignment and phylogenetic reconstruction Master of Science Sept. 2006 Sept. 2008 University of Waterloo, Waterloo, Ontario, Canada, Department of Electrical and Computer Engineering Advisors: Prof. Ladan Tahvildari Thesis: A Bayesian Framework for Software Regression Testing University of Tehran, Tehran, Iran Department of Electrical and Computer Engineering Bachelor of Science Sept. 2001 Dec. 2005 Computer engineering, specialized in software engineering. Major Awards Other Awards Research Funding Alfred P. Sloan Research Fellow. 2017 Honorable Mention for the 2015 ACM Doctoral Dissertation Award. 2016 Howard Hughes Medical Institute International Student Fellowship. 2012-2015 National Science and Engineering Research Council of Canada (NSERC), Canada Graduate Scholarship (CGS-D) converted to PGS-D outside Canada. 2011-2012 University of Texas at Austin, Computer Science, Bert Kay Dissertation Award, 2015 University of Texas at Austin, College of Natural Sci., Dean s Excellence Award, 2011 University of Texas at Austin, Graduate Dean s Prestigious Fellowship Supplement Award, 2011, 2012, 2013, 2014 University of Texas at Austin, MCD fellowship (I declined to take CGS instead) NSF-1565862: CRII: III: Using genomic context to understand evolutionary... 2016 NIH-CFAR: Accuracy of HIV transmission network reconstruction methods. 2016 1

Publications 1. Erfan Sayyari, James B Whitfield, and Siavash Mirarab. Fragmentary Gene Sequences Negatively Impact Gene Tree and Species Tree Reconstruction. Molecular Biology and Evolution, pages msx261 msx261, oct 2017. 2. Luke R. Thompson, Jon G. Sanders, Daniel McDonald, Amnon Amir, Joshua Ladau, Kenneth J. Locey, Robert J. Prill, Anupriya Tripathi, Sean M. Gibbons, Gail Ackermann, Jose A. Navas-Molina, Stefan Janssen, Evguenia Kopylova, Yoshiki Vázquez-Baeza, Antonio González, James T. Morton, Siavash Mirarab, Zhenjiang Zech Xu, Lingjing Jiang, Mohamed F. Haroon, Jad Kanbar, Qiyun Zhu, Se Jin Song, Tomasz Kosciolek, Nicholas A. Bokulich, Joshua Lefler, Colin J. Brislawn, Gregory Humphrey, Sarah M. Owens, Jarrad Hampton-Marcell, Donna Berg-Lyons, Valerie McKenzie, Noah Fierer, Jed A. Fuhrman, Aaron Clauset, Rick L. Stevens, Ashley Shade, Katherine S. Pollard, Kelly D. Goodwin, Janet K. Jansson, Jack A. Gilbert, Rob Knight, and The Earth Microbiome Project Consortium. A communal catalogue reveals Earth s multiscale microbial diversity. Nature, nov 2017. 3. Uyen Mai and Siavash Mirarab. TreeShrink: Efficient Detection of Outlier Tree Leaves. In Joao Meidanis and Luay Nakhleh, editors, Lecture Notes in Computer Science, volume 10562 LNBI, pages 116 140. Springer International Publishing, 2017. 4. Chao Zhang, Erfan Sayyari, and Siavash Mirarab. ASTRAL-III: Increased Scalability and Impacts of Contracting Low Support Branches. In Joao Meidanis and Luay Nakhleh, editors, Lecture Notes in Computer Science, volume 10562 LNBI, pages 53 75. Springer International Publishing, 2017. 5. Uyen Mai, Erfan Sayyari, and Siavash Mirarab. Minimum variance rooting of phylogenetic trees and implications for species tree reconstruction. PLOS ONE, 12(8):e0182238, 2017. 6. Shubhanshu Shekhar, Sebastien Roch, and Siavash Mirarab. Species tree estimation using ASTRAL: how many genes are enough? IEEE/ACM Transactions on Computational Biology and Bioinformatics, PP(99):1 1, 2017. 7. Siavash Mirarab. Phylogenomics: Constrained gene tree inference. Nature Ecology & Evolution, 1:0056, 2017. 8. Erfan Sayyari and Siavash Mirarab. Anchoring quartet-based phylogenetic distances and applications to species tree reconstruction. BMC Genomics, 17(S10):101 113, 2016. 9. Erfan Sayyari and Siavash Mirarab. Fast Coalescent-Based Computation of Local Branch Support from Quartet Frequencies. Molecular Biology and Evolution, 33(7):1654 1668, 2016. * co-first authorship 2

10. Nam Nguyen, Michael Nute, Siavash Mirarab, and Tandy Warnow. HIPPI: Highly Accurate Protein Family Classification with Ensembles of HMMs. BMC Genomics, in press, 2016. 11. James E Tarver, Mario Dos Reis, Siavash Mirarab, Raymond J Moran, Sean Parker, Joseph E O Reilly, Benjamin L King, Mary J O Connell, Robert J Asher, Tandy Warnow, Kevin J Peterson, Philip C J Donoghue, and Davide Pisani. The Interrelationships of Placental Mammals and the Limits of Phylogenetic Inference. Genome biology and evolution, page evv261, 2016. 12. Siavash Mirarab, Md. Shamsuzzoha Bayzid, Bastien Boussau, and Tandy Warnow. Response to Comment on Statistical binning enables an accurate coalescent-based estimation of the avian tree. Science, 350(6257):171, 2015. 13. Joel Cracraft, Peter Houde, Simon Y W Ho, David P Mindell, Jon Fjeldså, Bent Lindow, Scott V Edwards, Carsten Rahbek, Siavash Mirarab, Tandy Warnow, M Thomas P Gilbert, Guojie Zhang, Edward L Braun, and Erich D Jarvis. Response to Comment on Whole-genome analyses resolve early branches in the tree of life of modern birds. Science, 349(6255):1460, 2015. 14. Ruth Davidson, Pranjal Vachaspati, Siavash Mirarab, and Tandy Warnow. Phylogenomic species tree estimation in the presence of incomplete lineage sorting and horizontal gene transfer. BMC Genomics, 16(Suppl 10):S1, 2015. 15. Jed Chou, Ashu Gupta, Shashank Yaduvanshi, Ruth Davidson, Mike Nute, Siavash Mirarab, and Tandy Warnow. A comparative study of SVDquartets and other coalescent-based species tree estimation methods. BMC Genomics, 16(Suppl 10):S2, 2015. 16. Siavash Mirarab and Tandy Warnow. ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes. Bioinformatics, 31(12):i44 i52, 2015. 17. Nam Nguyen, Siavash Mirarab, Keerthana Kumar, and Tandy Warnow. Ultralarge alignments using phylogeny-aware profiles. Genome Biology, 16(1):124, 2015. 18. Md Shamsuzzoha Bayzid, Siavash Mirarab, Bastien Boussau, and Tandy Warnow. Weighted Statistical Binning: Enabling Statistically Consistent Genome- Scale Phylogenetic Analyses. PLoS ONE, 10(6):e0129183, 2015. 19. Erich D Jarvis, Siavash Mirarab, Andre J Aberer, Bo Li, Peter Houde, Cai Li, Simon Y W Ho, Brant C Faircloth, Benoit Nabholz, and Jason T Howard. Phylogenomic analyses data of the avian phylogenomics project. GigaScience, 4(1):4, 2015. 20. Erich D Jarvis, Siavash Mirarab*, 100 other authors, Edward L Braun, Tandy Warnow, Wang Jun, M Thomas P Gilbert, and Guojie Zhang. Whole-genome 3

analyses resolve early branches in the tree of life of modern birds. 346(6215):1320 1331, 2014. Science, 21. Siavash Mirarab, Md. Shamsuzzoha Bayzid, Bastien Boussau, and Tandy Warnow. Statistical binning enables an accurate coalescent-based estimation of the avian tree. Science, 346(6215):1250463, 2014. 22. Naim Matasci, Ling-Hong Hung, Zhixiang Yan, Eric J Carpenter, Norman J Wickett, Siavash Mirarab, Nam Nguyen, Tandy Warnow, Saravanaraj Ayyampalayam, and Michael Barker. Data access for the 1,000 Plants (1KP) project. GigaScience, 3(1):17, 2014. 23. Norman J. Wickett, Siavash Mirarab*, Nam Nguyen, Tandy Warnow, Eric Carpenter, Naim Matasci, Saravanaraj Ayyampalayam, Michael Barker, J. Gordon Burleigh, Matthew A. Gitzendanner, Brad R. Ruhfel, Eric Wafula, Joshua P. Der, Sean W. Graham, Sarah Mathews, Michael Melkonian, Douglas E. Soltish, Pamela S. Soltish, Nicholas W. Miles, Carl J. Rothfels, Lisa Pokorny, A. Jonathan Shaw, Lisa DeGironimo, Dennis W. Stevenson, Barbara Surek, Juan Carlos Villarreal, Béatrice Rourev, Hervé Philippe, Claude W. depamphilis, Tao Chen, Michael K. Deyholos, Regina S. Baucom, Toni M. Kutchan, Megan M. Augustin, Jun Wang, Yong Zhang, Zhijian Tian, Zhixiang Yan, Xiaolei Wu, Xiao Sun, Gane Ka-Shu Wong, and James Leebens-Mack. Phylotranscriptomic analysis of the origin and early diversification of land plants. Proceedings of the National Academy of Sciences (PNAS), 111(45):E4859 4868, 2014. 24. Siavash Mirarab, Md. Shamsuzzoha Bayzid, and Tandy Warnow. Evaluating Summary Methods for Multilocus Species Tree Estimation in the Presence of Incomplete Lineage Sorting. Systematic Biology, 65(3):366 380, 2014. 25. Siavash Mirarab, Nam Nguyen, Sheng Guo, Li-San Wang, Junhyong Kim, and Tandy Warnow. PASTA: Ultra-Large Multiple Sequence Alignment for Nucleotide and Amino-Acid Sequences. Journal of Computational Biology, 22(05):377 386, 2015. 26. Siavash Mirarab, Nam Nguyen, and Tandy Warnow. PASTA: ultra-large multiple sequence alignment. In Proceedings of the 18th International Conference on Research in Computational Molecular Biology (RECOMB), pages 177 191, 2014. 27. Siavash Mirarab, Rezwana Reaz, Md. Shamsuzzoha Bayzid, Théo Zimmermann, M Shel Swenson, and Tandy Warnow. ASTRAL: Genome-Scale Coalescent-Based Species Tree. Bioinformatics (ECCB), 30(17):i541 i548, 2014. 28. Nam Nguyen, Siavash Mirarab, Bo Liu, Mihai Pop, and Tandy Warnow. TIPP: Taxonomic Identification and Phylogenetic Profiling. Bioinformatics, 30(24):3548 3555, 2014. 4

29. Théo Zimmermann, Siavash Mirarab, and Tandy Warnow. BBCA: improving the scalability of *BEAST using random binning. BMC Genomics, 15(Suppl 6):S11, 2014. 30. Arlin Stoltzfus, Hilmar Lapp, Naim Matasci, Helena Deus, Brian Sidlauskas, Christian M Zmasek, Gaurav Vaidya, Enrico Pontelli, Karen Cranston, Rutger Vos, Campbell O Webb, Luke J Harmon, Megan Pirrung, Brian O Meara, Matthew W Pennell, Siavash Mirarab, Michael S Rosenberg, James P Balhoff, Holly M Bik, Tracy A Heath, Peter E Midford, Joseph W Brown, Emily Jane McTavish, Jeet Sukumaran, Mark Westneat, Michael E Alfaro, Aaron Steele, and Greg Jordan. Phylotastic! Making tree-of-life knowledge accessible, reusable and convenient. BMC bioinformatics, 14(1):158, 2013. 31. Md. Shamsuzzoha Bayzid, Siavash Mirarab, and Tandy Warnow. Inferring optimal species trees under gene duplication and loss. In Proceedings of the Pacific Symposium on Biocomputing (PSB), pages 250 61, 2013. 32. Nam Nguyen, Siavash Mirarab, and Tandy Warnow. MRL and SuperFine+ MRL: new supertree methods. Algorithms for Molecular Biology, 7(1):3, 2012. 33. Siavash Mirarab, Nam Nguyen, and Tandy Warnow. SEPP: SATé-Enabled Phylogenetic Placement. In Proceedings of the Pacific Symposium on Biocomputing (PSB), pages 247 58, 2012. 34. Siavash Mirarab, Soroush Akhlaghi, and Ladan Tahvildari. Size-constrained regression test case selection using multicriteria optimization. IEEE Transactions on Software Engineering, 38(4):936 956, 2012. 35. Siavash Mirarab and Tandy Warnow. FastSP: linear time calculation of alignment accuracy. Bioinformatics, 27:3250 8, 2011. 36. Hyunsook Do, Siavash Mirarab, Ladan Tahvildari, and Gregg Rothermel. The effects of time constraints on test case prioritization: A series of controlled experiments. IEEE Transactions on Software Engineering, 36(5):593 617, 2010. 37. Siavash Mirarab and Ladan Tahvildari. An empirical study on bayesian network-based approach for test case prioritization. In Proceedings of the 1st International Conference on Software Testing, Verification, and Validation (ICST), pages 278 287. IEEE, 2008. 38. Siavash Mirarab, Afshar Ganjali, Ladan Tahvildari, Shimin Li, Weining Liu, and Mike Morrissey. A requirement-based software testing framework: An industrial practice. In Proceedings of the 24th International Conference on Software Maintenance (ICSM), pages 452 455. IEEE, 2008. 39. Hyunsook Do, Siavash Mirarab, Ladan Tahvildari, and Gregg Rothermel. An empirical study of the effect of time constraints on the cost-benefits of regression 5

testing. In Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE), pages 71 82. ACM, 2008. 40. Mehdi Amoui, Mazeiar Salehie, Siavash Mirarab, and Ladan Tahvildari. Adaptive action selection in autonomic software using reinforcement learning. In Proceedings of the Fourth International Conference on Autonomic and Autonomous Systems (ICAS), pages 175 181. IEEE, 2008. 41. Siavash Mirarab and Ladan Tahvildari. A prioritization approach for software test cases based on bayesian networks. In Fundamental Approaches to Software Engineering (FASE), pages 276 290. Springer, 2007. 42. Siavash Mirarab, Alaa Hassouna, and Ladan Tahvildari. Using bayesian belief networks to predict change propagation in software systems. In Proceedings of the 15th International Conference on Program Comprehension (ICPC), pages 177 188. IEEE, 2007. 43. Mehdi Amoui, Siavash Mirarab, Sepand Ansari, and Caro Lucas. A genetic algorithm approach to design evolution using design pattern transformation. Journal of Information Technology and Intelligent Computing, 1(2):235 244, 2006. Software (Publicly available) ASTRAL (https://github.com/smirarab/astral) Coalescent-based species tree estimation from gene trees PASTA (https://github.com/smirarab/pasta) Multiple sequence alignment of ultra-large datasets (up to a million sequences) SEPP,TIPP (https://github.com/smirarab/sepp) Phylogenetic placement and taxonomic identification of metagenomic reads UPP (https://github.com/smirarab/sepp/blob/master/readme.upp.md) Multiple sequences alignment using families of HMMs for ultra-large datasets FastSP (https://github.com/smirarab/fastsp) Fast comparison of very large multiple sequence alignments See https://github.com/smirarab/?tab=repositories for more. Professional Experience International Business Machines (IBM), Vancouver, British Columbia, Canada IT Specialist Sept. 2008 Dec. 2010 Worked on a software platform for implementing implantable wireless pace-maker devices for a large US-based biomedical company. Teaching Instructor (University of California, San Diego): Statistical learning in bioinformatics. Winter 2016 Algorithms for biological data analysis. Winter 2016 Introduction to Computer Engineering Winter, Spring 2016, Winter 2017 6

Teaching Assistance: Software Testing and Quality Assurance, University of Waterloo. Winter 2007 Scientific & Technical Communication, University of Tehran. Fall 2005 Workshops, Tutorials, and Short Courses: Ann Arbor, Michigan, Phylogenomics Symposium and Software School 2015 UT Austin, Summer school on Phylogenomics and Metagenomics Summer 2014 Evolution meetings (NC), Software School on ASTRAL and PASTA Summer 2014 UT Austin, Workshop on New Methods for Phylogeny and Alignment Estimation, Tutorials on SEPP and SATe Winter 2013 Highlights and Invited Talks Broad Inst., MA, Taxonomic Profiling using Scalable Phylogenetic Placement 2017 RECOMB, Warsaw, Poland, Statistical binning enables an accurate... 2015 IPAM, UCLA, Ultra-large multiple sequence alignments. 2015 INFORMS, Philadelphia, Reconstruction of species trees using genomic data. 2015 MolPhy-3, Moscow, Russia, SEPP and TIPP: phylogenetic placement and taxon identification methods for metagenomic. 2012 Professional Services Program Committee: ISMB/ECCB 2015, 2017 Paper Review: Nature Ecology and Evolution, GigaScience, Journal of ACM, IEEE Transaction on Computational Biology and Bioinformatics, Theoretical Population Genetics, Bioinformatics, BMC Bioinformatics, Journal of ACM, ISMB, RECOMB- CG, Transactions on Software Engineering, and ICPC 2007-2016 7

Truong Nguyen Electrical & Computer Engineering University of California, San Diego Phone: 858-822-5554 9500 Gilman Drive, MC 0407 tqn001@eng.ucsd.edu La Jolla, CA 92093-0407 Professional Preparation: Institution Major Degree & Year California Institute of Technology Electrical Engineering PhD 1989 California Institute of Technology Electrical Engineering MSc 1986 California Institute of Technology Electrical Engineering BS 1985 Appointments: Institution Department Duration University of California, San Diego Electrical & Computer Engineering 2001- present Boston University Electrical & Computer Engineering 1996-2001 University of Wisconsin, Madison Electrical & Computer Engineering 1994-1998 Massachusetts Inst. of Technology Lincoln Laboratory 1989-1994 Honors & Awards: Best Undergraduate Teaching, UCSD, ECE Dept., 2006, 2008, 2010 IEEE Fellows, 2006 Boston Univ. Technology Award, 1999 NSF Career Award, 1995-1998 IEEE Signal Processing Society's 1992 Paper Award - Image and Multidimensional Area Selected Publications: 1. S. Tripathi, B. Kang, G. Dane, and T. Nguyen, Low-Complexity Object Detection with Deep Convolutional Neural Network for Embedded Systems, Proceeding of SPIE 2017. 2. J. Dai and T. Nguyen, View Synthesis with Hierarchical Clustering based Occlusion Filling, Proceeding of ICIP 2017. 3. H. Yue, J. Liu, J. Yang, T. Nguyen, and C. Hou, Image Noise Estimation and Removal Considering the Bayer Pattern of Noise Variance, Proceeding of ICIP 2017. 4. Y. Lee, K. Hirakawa, and T. Q. Nguyen, Lossless Compression of CFA Sampled Image using Decorrelated Mallat Wavelet Packet Decomposition, Proceedings of ICIP, 2017. 5. S. Parameswaran, E. Luo, and T. Q. Nguyen, Targeted Video Denoising for Decompressed Videos, Proceedings of ICIP 2017. 6. Y. Lee, K. Hirakawa, and T. Nguyen, Joint Defogging and Demosaicking, Proceedings of ICIP, 2017. 7. S. Parameswaran, E. Luo, C. Deledalle, and T. Nguyen, Fast External Denoising using Prelearned Transformations, Proceedings of CVPRW 2017. 8. S. Tripathi, G. Dane, B. Kang, V. Bhaskaran, and T. Nguyen, LCDet: Low-Complexity Fully- Convolutional Neural Networks for Object Detection for Embedded Systems, Proceedings of CVPRW 2017. 9. Y. Lee, K. Hirakawa and T. Q. Nguyen, Joint Defogging and Demosaicking, IEEE Trans. On Image Processing, Vol. 26, Issue 6, pp. 3051-3066, June 2017. 10. I. Fedorov, B. Rao and T. Nguyen, Multimodal Sparse Bayesian Dictionary Learning Applied To Multimodal Data Classification, Proceeding of ICASSP 2017. 11. Y. Lee, K. Hirakawa and T. Q. Nguyen, Joint Defogging and Demosaicking, IEEE Trans. On Image Processing, Vol. 26, Issue 6, pp. 3051-3066, June 2017. Biographical Sketches

12. C. Ren, X. He and T. Q. Nguyen, Single Image Super-Resolution via Adaptive High- Dimensional Non-Local Total Variation and Adaptive Geometric Feature, IEEE Trans. on Image Processing, Vol. 26, Issue 1, pp. 90-106, January 2017. 13. S. Tripathi, Z. Lipton, S. Belongie and T. Nguyen, Context Matters: Refining Object Detection in Video with Recurrent Neural Networks, BMCV 2016. 14. I. Fedorov, R. Giri, B.D. Rao and T. Q. Nguyen, Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition, Proceeding of ICIP 2016. 15. E. Luo, S. H. Chan, and T. Q. Nguyen, Adaptive Image Denoising by Mixture Adaptation, IEEE Trans. on Image Processing, Vol. 25, Issue 10, pp. 4489-4503, October 2016. 16. L. K.Liu and T. Q. Nguyen, A Framework for Depth Video Reconstruction from a Subset of Samples and Its Applications, IEEE Trans. on Image Processing, Vol. 25, Issue 10, pp. 4873-4887, October 2016. 17. S. Parameswaran, E. Luo and T. Q. Nguyen, Patch Matching for Image Denoising Using Neighborhood-based Collaborative Filtering, IEEE Trans. on Circuits and Systems for Video Technology, September 2016. 18. C. Ren, X. He, Q. Teng. Y. Wu and T. Q. Nguyen, Single Image Super-Resolution using Local Geometric Duality and Non-Local Similarity, IEEE Trans. on Image Processing, Vol. 25, Issue 5, pp. 2168-2183, May 2016. 19. B. Kang, S. Tripathi and T. A. Nguyen, Real-time Sign Language Finger Spelling Recognition using Convolution Neural Networks from Depth Map, ACPR 2015. 20. S. Tripathi, S. Belongie, Y. B. Hwang and T. Q. Nguyen, Beyond Semantic Image Segmentation: Exploring Inference Efficiency in Video, Proceeding of ISOCC 2015. 21. S. Tripathi, S. Berlongie and T. Q. Nguyen, Beyond Semantic Image Segmentation: Exploring Efficient Inference in Video, CVPR WiCV Workshop 2015, June 11 2015. 22. M. Zeng and Truong Q. Nguyen, Analysis of Crosstalk in 3D Circularly Polarized LCDs Depending on the Vertical Viewing Location, IEEE Trans. on Image Processing, Vol. 25, Issue 3, pp. 1070-1083, March 2016. 23. Y. Wu, X. He, B. Kang, H. Song and T. Q. Nguyen, Long-Range Motion Trajectories Extraction of Articulated Human Using Mesh Evolution, IEEE Signal Processing Letters, Vol. 23, Issue 4, pp. 507-511, April 2016. 24. Kyoung-Rok Lee and Truong Nguyen, Realistic Surface Geometry Reconstruction using a Hand-Held RGB-D Camera, Machine Vision and Applications, Springer, Vol. 27, Issue 3, pp. 377-385, April 2016. 25. Q. Yan, Y. Xu, X. Yang, and T. Q. Nguyen, Single Image Super-Resolution Based on Gradient Profile Sharpness, IEEE Trans. on Image Processing, Vol. 24, Issue 10, pp. 3187-3202, October 2015. 26. M. Zeng, A. Robinson and T. Q. Nguyen, Modeling, Prediction and Reduction of 3D Crosstalk in Circular Polarized Stereoscopic LCDs, IEEE Trans. on Image Processing, Vol. 24, No. 12, pp. 5516-5530, December 2015. 27. J. Chae, J. Lee, M. Lee, J. Han, T. Q. Nguyen and W. Yeo, Cubic Convolution Scaler Optimized for Local Property of Image Data, IEEE Trans. on Image Processing, Vol. 24, No. 12, pp. 4796 4809, December 2015. 28. Z. Lee, T. Q. Nguyen, " Multi-resolution Disparity Processing and Fusion for Large Highresolution Stereo Image", IEEE Trans. on Multimedia, Vol. 17, Issue 6, pp. 792-803, April 2015. 29. L.K. Liu, S. H. Chan and T. Q. Nguyen, Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling, IEEE Trans. on Image Processing, Vol. 24, Issue 6, pp. 1983-1996, June 2015. 30. E. Luo, S. H. Chan and T. Q. Nguyen, Adaptive Image Denoising by Targeted Databases, IEEE Trans. on Image Processing, Vol. 24, Issue 7, pp. 2167-2181, July 2015. 31. Gilbert Strang and Truong Nguyen, Wavelets and Filter Banks, Wellesley: Wellesley- Cambridge Press, (ISBN 0-9614088-7-1), January 1996. Biographical Sketches

ALON ORLITSKY ECE and CSE Departments, University of California, San Diego alon@ucsd.edu (858) 822-0228 http://alon.ucsd.edu Professional Preparation 1982-1986 Ph.D., Electrical Engineering, Stanford University, Stanford, CA 1981-1982 M.Sc., Electrical Engineering, Stanford University, Stanford, CA 1977-1981 B.Sc., Electrical Engineering, Ben-Gurion University, Israel 1977-1980 B.Sc., Mathematics, Ben-Gurion University, Israel Appointments 2011-2014 Director, Center for Wireless Communication 2007 - present Qualcomm Professor, University of California, San Diego 2006 - present Director, Information Theory and Applications Center 1997 - present Professor, ECE & CSE Departments, University of California, San Diego 1996-1997 Quantitative Analyst, D. E. Shaw and Company 1986-1996 Member of Technical Staff, AT&T Bell Laboratories 1992-1993 Adjunct Associate Professor, Columbia University Most-Related Publications 1. J. Acharya, H. Das, A. Orlitsky, and A.T. Suresh. A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions. In ICML, 2017. (Best paper honorable mention award: 1 best paper and 3 honorable mentions out of 1,701 submissions.) 2. A. Orlitsky, A.T. Suresh, and Y. Wu. Optimal prediction of the number of unseen species. In PNAS, Vol. 113, No. 47, Nov. 22, 2016. 3. M. Falahatgar, M.I. Ohannessian, and A. Orlitsky. Near-Optimal Smoothing of Structured Conditional Probability Matrices In NIPS, 2016. 4. A. Orlitsky and A.T. Suresh. Competitive distribution estimation: Why is Good-Turing good. In NIPS, 2015. (Outstanding paper award: 2 papers out of 1,838 submissions.) 5. S. Kamath, A. Orlitsky, V. Pichapati, and A.T. Suresh. On learning distributions from their samples. In JMLR-COLT, pages 764 796, 2015. Other Publications 1. M. Falahatgar, A. Orlitsky, V. Pichapati, and A.T. Suresh. Maximum Selection and Ranking under Noisy Comparisons. In ICML, 2017. 2. J. Acharya, A. Orlitsky, A.T. Suresh, and H. Tyagi. The complexity of estimating Rényi entropy. In SODA, pages 1855 1869, 2015. 3. M. Falahatgar, A. Jafarpour, A. Orlitsky, V. Pichapati, and A.T. Suresh. Universal compression of power-law distributions. In ISIT, pages 2001 2005, 2015. 4. M. Falahatgar, A. Jafarpour, A. Orlitsky, V. Pichapati, and A.T. Suresh. Faster algorithms for testing under conditional sampling. In JMLR-COLT, pages 607-636, 2015.

5. A. T. Suresh, A. Orlitsky, J. Acharya, and A. Jafarpour. Near-optimal-sample estimators for spherical gaussian mixtures. In NIPS, pages 1395 1403, 2014. Synergistic Activities Paper Awards: ICML 2017 Best Paper Honorable Mention (1 best paper, 3 HM s of 1,701 submissions) NIPS 2015 Outstanding Paper Award (2 papers out of 1,838 submissions) Co-author and advisor of Student-Paper-Award Recipient, ISIT 2010 IEEE Transactions on Information Theory Paper Award 2006 Co-author and advisor of Student-Paper-Award Recipient, DCC 2003 IEEE W.R.G. Baker Paper Award, 1992 Plenary Talks: STOC 2017 (one of 11 best theory talks), IZS 2014, WITMSE 2010 ISIT 2008, The Learning Workshop 2008, ITW 2007, UAI 2004 IT Society: 2nd VP, 2014; 1st VP, 2015; President, 2016; Junior Past President, 2017 ITA Center: Mentoring: Director, ITA Workshop co-organizer, 2006 present As ITA director helped hire and co-mentor 16 ITA postdocs 4 of the postdocs are women, 11 out of 15 who finished are now faculty IT Society Mentoring IT Program, 2008-2013 Mentored 5 ITA undergraduate webmasters, including one now corporate VP

Piya Pal Department of Electrical and Computer Engineering University of California, San Diego, La Jolla, CA 92093 https://sites.google.com/a/eng.ucsd.edu/piya-pal E-mail: pipal@ucsd.edu Phone: (858)-246-2565 Professional Appointments University of California, San Diego Assistant Professor of Electrical and Computer Engineering July 2016 - Present University of Maryland, College Park Jan. 2014 - July 2016 Assistant Professor of Electrical and Computer Engineering Affiliate Faculty: Institute for Systems Research California Institute of Technology July 2013 - Nov. 2013 Postdoctoral Scholar in Electrical Engineering Advisor: Prof. P. P. Vaidyanathan Microsoft Research, Redmond, USA June - Oct. 2012 Research Intern, Mentor: Dr. Henrique Malvar Education California Institute of Technology Sep. 2007 - June 2013 Ph.D. in Electrical Engineering Advisor: Prof. P. P. Vaidyanathan California Institute of Technology 2007-2008 M.S. in Electrical Engineering Indian Institute of Technology - Kharagpur 2003-2007 B. Tech in Electronics and Electrical Communication Engineering Selected Honors And Awards Best Student Paper Award (First Place) March 2017 IEEE ICASSP New Orleans, Louisiana. NSF Faculty Early Career Development (CAREER) Award 2016 2014 Charles and Ellen Wilts Prize, Caltech 2014 Awarded to outstanding Ph.D Thesis in Electrical Engineering. Best Student Paper Award January 2011 IEEE DSP Workshop Sedona, Az. Best Student Paper Award (third position), Nov. 2011 45 th Asilomar Conference on Signals, Systems and Computers Pacific Grove, CA. Research Interests Statistical Signal Processing for High Dimensional Signals. Sparse Sampling, representation and reconstruction. Inverse Problems in High Resolution Imaging. Array Processing with applications in smart wireless devices and tracking systems.

Selected Professional Service Elected Member of the IEEE Signal Processing Society s Sensor Array And Multichannel (SAM) Technical Committee, (2016-2018). Track Chair for Array Signal Processing, Asilomar Conference on Signals, Systems and Computers, 2017. Guest Editor, Digital Signal Processing (Elsevier), Special Issue on Coprime Sampling and Arrays, 2016. Publicity Chair: IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016. Member of Technical Program Committee for IEEE SAM 2014, 2016, ICASSP 2013-2016, IEEE CAM- SAP 2013, 2015, IEEE GlobalSIP 2015-2016, EUSIPCO 2015. Journal Reviewing: IEEE Transactions on Signal Processing, IEEE Transactions on Information Theory, IEEE Journal on Selected Topics in Signal Processing, Proceedings of the IEEE, IEEE Signal Processing Letters, IEEE Transactions on Circuits and Systems, IEEE Transactions on Aerospace and Electronic Systems, EURASIP Journal on Signal Processing, Signal Processing. Publication: Thesis and Book Chapter P. P. Vaidyanathan and Piya Pal, Coprime sampling and arrays in one and multiple dimensions, Chapter 5 in Multiscale Signal Analysis and Modeling, Edited by X. A. Shen and A. I. Zayed, Springer, 2012. Piya Pal, New directions in sparse sampling and estimation for underdetermined systems, Ph.D Thesis, Caltech, 2013. Journal Publications 1. Heng Qiao and Piya Pal, On Maximum Likelihood Methods For Localizing More Sources than Sensors, accepted, IEEE Signal Processing Letters, 2017. 2. Heng Qiao and Piya Pal, Gridless Line Spectrum Estimation and Low-Rank Toeplitz Matrix Compression Using Structured Samplers: A Regularization-Free Approach, IEEE Transactions on Signal Processing, vol. 65, no. 9, pp. 2221-2236, May, 2017. 3. Ali Koochakzadeh and Piya Pal, Performance of Uniform and Sparse Non-Uniform Samplers in Presence of Modeling Errors: A Cramer-Rao Bound Based Study, IEEE Transactions on Signal Processing, vol. 65, no. 6, pp. 1607-1621, March, 2017. 4. Ali Koochakzadeh and Piya Pal, Sparse Source Localization Using Perturbed Arrays via Bi-Affine Modeling, Digital Signal Processing (Elsevier), Special Issue on Coprime Sampling and Arrays, vol. 61, pp. 15-25, Feb. 2017. 5. A. Kazemipour, S. Miran, Piya Pal, B. Babadi, and M. Wu, Sampling Requirements for Stable Autoregressive Estimation, IEEE Transactions on Signal Processing, vol. 65, no. 9, pp. 2333-2347, May, 2017. 6. Ali Koochakzadeh and Piya Pal, Cramér-Rao Bounds for Underdetermined Source Localization, accepted, IEEE Signal Processing Letters, May 2016. 7. Heng Qiao and Piya Pal, Generalized Nested Sampling for Compressing Low Rank Toeplitz Matrices, IEEE Signal Processing Letters, vol. 22, no. 11, pp. 1844-1848, Nov. 2015. 8. Piya Pal and P. P. Vaidyanathan, Pushing the Limits of Sparse Support Recovery Using Correlation Information, IEEE Trans. on Signal Processing, vol. 63, no. 3, pp. 711-726, Feb. 2015. 9. Qi Cheng, Piya Pal, Masashi Tsuji and Yingbo Hua, An MDL Algorithm for Detecting More Sources Than Sensors Using Outer-Products of Array Output, IEEE Trans. on Signal Processing, vol. 62, no. 24, pp. 6438-6453, Dec. 2014. 10. Piya Pal, and P. P. Vaidyanathan, A Grid-Less Approach to Underdetermined Direction of Arrival Estimation Via Low Rank Matrix Denoising, IEEE Signal Processing Letters, vol. 21, no. 6, pp. 737-741, June 2014.

11. Piya Pal and P. P. Vaidyanathan, Multiple Level Nested Array: An efficient geometry for 2qth order cumulant based array processing, IEEE Transactions on Signal Processing, vol.60, no.3, pp.1253-1269, March 2012. 12. Piya Pal and P. P. Vaidyanathan, Nested Arrays in Two Dimensions, Part I: Geometrical Considerations, IEEE Trans. on Signal Processing, vol. 60, no. 9, pp. 4694-4705, Sept. 2012. 13. Piya Pal and P. P. Vaidyanathan, Nested Arrays in Two Dimensions, Part II: Application to Two Dimensional Array Processing, IEEE Trans. on Signal Processing, vol. 60, no. 9, pp. 4706-4718, Sept. 2012. 14. P. P. Vaidyanathan and Piya Pal, Sparse Sensing With Co-Prime Samplers and Arrays, IEEE Trans. on Signal Processing, vol. 59, pp. 573 586, Feb. 2011. 15. Piya Pal and P. P. Vaidyanathan, Coprimality of Certain Families of Integer Matrices, IEEE Trans. on Signal Processing, vol. 59, pp. 1481 1490, April 2011. 16. P. P. Vaidyanathan and Piya Pal, Theory of sparse coprime sensing in multiple dimensions, IEEE Transactions on Signal Processing, Vol. 59, No. 8, Aug. 2011. 17. P. P. Vaidyanathan and Piya Pal, A General Approach to Coprime Pairs of Matrices, Based on Minors, IEEE Transactions on Signal Processing, vol. 59, No. 8, Aug. 2011. 18. P. P. Vaidyanathan and Piya Pal, Generating New Commuting Coprime Matrix Pairs From Known Pairs, Signal Processing Letters, IEEE, vol.18, no.5, pp. 303-306, May 2011. 19. P. P. Vaidyanathan and Piya Pal, System Identification With Sparse Coprime Sensing, Signal Processing Letters, IEEE, vol.17, no.10, pp. 823-826, Oct. 2010. 20. Piya Pal and P. P. Vaidyanathan, Nested arrays: a novel approach to array processing with enhanced degrees of freedom, IEEE Trans. on Signal Processing, vol. 58, pp. 4167 4181, August 2010. Conference Publications 1. Heng Qiao and Piya Pal, Finite Sample Analysis of Covariance Compression Using Structured Samplers, accepted, IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016. 2. Robert Lee, Piya Pal and Linda Mullen, Theoretical And Experimental Study of Sub-Nyquist FMCW LIDAR systems, to be presented at the Conference on Compressive Sensing V: From Diverse Modalities to Big Data Analytics, SPIE Commercial + Scientific Sensing and Imaging, Baltimore, April 2016. 3. Ali Koochakzadeh and Piya Pal, An entropy-driven matrix completion (E-MC) approach to complex network mapping, to be presented at the Conference on Compressive Sensing V: From Diverse Modalities to Big Data Analytics, SPIE Commercial + Scientific Sensing and Imaging, Baltimore, April 2016. 4. Heng Qiao and Piya Pal, Sparse Phase Retrieval with Near Minimal Measurements: A Structured Sampling Based Approach, to be presented at IEEE ICASSP 2016. 5. Chun-Lin Liu, Piya Pal and P. P. Vaidyanathan, Coprime Coarray Interpolation for DOA Estimation via Nuclear Norm Minimization, to be presented at ISCAS, 2016. 6. Piya Pal, Dictionary Learning from Quadratic Measurements in Block Sparse Models, to be presented at Asilomar Conference on Signals, Systems, and Computers, 2015. 7. Ali Koochakzadeh and Piya Pal, Rank Deficiency and Sparsity in Partially Observed Multiple Measurement Vector Models, to be presented at Asilomar Conference on Signals, Systems and Computers, 2015. 8. Heng Qiao and Piya Pal, Sparse Phase Retrieval Using Partial Nested Fourier Samplers, Proc. of IEEE GlobalSIP, Atlanta, Dec. 2015. 9. Robert Lee, Linda Mullen, Piya Pal and David Illig, Time of Flight Measurements for Optically Illuminated Underwater Targets Using Compressive Sampling and Sparse Reconstruction, MTS/IEEE Oceans 2015, Washington D.C., 2015.

10. Ali Koochakzadeh and Piya Pal, Sparse Source Localization in Presence of Co-Array Perturbations, Proc. Sampling Theory and Applications (SampTA), Washington D.C., USA, 2015. 11. Ali Koochakzadeh and Piya Pal, On the Robustness of Co-Prime Sampling, Invited paper at EU- SIPCO, 2015. 12. Heng Qiao and Piya Pal, Generalized Nested Sampling for Compression and Exact Recovery of Symmetric Toeplitz Matrices, IEEE GlobalSIP Symposium on Information Processing for Big Data, Atlanta, Dec. 3-5, 2014. 13. Piya Pal and P. P. Vaidyanathan, Gridless methods for underdetermined source estimation, Proc. Asil. Conf. Sig., Sys., and Comp., Monterey, CA, Nov. 2014. 14. Piya Pal and P. P. Vaidyanathan, Soft-Thresholding for Spectrum Sensing with Coprime Samplers, invited paper at IEEE SAM, 2014, Spain, June 22-25, 2014. 15. Piya Pal and P. P. Vaidyanathan, Parameter Identifiability in Sparse Bayesian Learning, ICASSP, Florence, Italy, May 4-9, 2014. 16. Piya Pal and P. P. Vaidyanathan, Conditions for Identifiability in Sparse Spatial Spectrum Sensing, Invited paper at EUSIPCO, Marrakech, Sep. 2013. 17. Piya Pal and P. P. Vaidyanathan, Correlation Aware Sparse Support Recovery: Gaussian Sources, presented at ICASSP, Vancouver, May 2013. 18. Piya Pal and P. P. Vaidyanathan, Correlation Aware Techniques for Sparse Support Recovery, IEEE Statistical Signal Processing Workshop (SSP), 2012, pp.53-56, 5-8 Aug. 2012. 19. Piya Pal and P. P. Vaidyanathan, On Application of LASSO for Sparse Support Recovery With Imperfect Correlation Awareness, 46th Asilomar Conference on Signals, Systems and Computers, 2012, Nov. 4-7, 2012. 20. Piya Pal and P. P. Vaidyanathan, Non-Uniform Linear Arrays for Improved Identifiability in Cumulant Based DOA Estimation, 45th Asilomar Conference on Signals, Systems and Computers, 2011 (Winner of the 3rd Prize in Best Student Paper Award Contest, November, 2011). 21. Piya Pal and P. P. Vaidyanathan, Coprime sampling and the MUSIC algorithm, IEEE DSP Workshop, Sedona, AZ, January 2011 (Winner of Best Student Paper Award). 22. Piya Pal and P. P. Vaidyanathan, Two Dimensional Nested Arrays on Lattices, ICASSP, May, Prague, 2011. 23. Piya Pal and P. P. Vaidyanathan, A novel array structure for directions-of-arrival estimation with increased degrees of freedom, Proc. ICASSP., Dallas, March 2010. 24. Piya Pal and P. P. Vaidyanathan, Beamforming using passive nested arrays of sensors, Proc. IEEE Int. Symp. Circuits and Systems, Paris, May-June 2010. 25. Piya Pal and P. P. Vaidyanathan, Efficient Frequency Invariant Beamforming using Virtual Arrays, 44th Asilomar Conf. on Signals, Systems and Computers, 2010. 26. Piya Pal and P.P. Vaidyanathan, A novel autofocusing approach for estimating directions-of-arrival of wideband signals, 43rd Asilomar Conf. on Signals, Systems and Computers, pp.1663-1667, Nov. 2009. 27. Piya Pal and P. P. Vaidyanathan, Frequency invariant MVDR Beamforming without filters and implementation using MIMO radar, Proc. ICASSP, Taipei, April 2009. (finalist for Best Student Paper Award). 28. P. P. Vaidyanathan, Piya Pal, and Chun-Yang Chen, MIMO radar with broad band waveforms: smearing filter banks and 2D virtual arrays, Proc. 42nd Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, Oct. 2008. 29. P. P. Vaidyanathan and Piya Pal, MIMO radar, SIMO radar, and IFIR radar: a comparison, Proc. 43rd Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, Nov. 2009. 30. P. P. Vaidyanathan and Piya Pal, Sparse sensing with coprime arrays, Proc. 44th Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, Nov. 2010.

31. P. P. Vaidyanathan and Piya Pal, Sparse coprime sensing with multidimensional lattice arrays, 14th IEEE DSP workshop, Sedona, AZ, Jan. 2011. 32. P. P. Vaidyanathan and Piya Pal, Adjugate pairs of sparse arrays for sampling two-dimensional signals, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc., Prague, Czech Republic, May 2011. 33. P. P. Vaidyanathan and Piya Pal, Coprime sampling for system stabilization with FIR multirate controllers, Proc. 45th Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, Nov. 2011. 34. P. P. Vaidyanathan and Piya Pal, Direct-MUSIC on sparse arrays, IEEE Intl. Conf. on Signal Proc. and Comm., Bangalore, India, July 2012. 35. P. P. Vaidyanathan and Piya Pal, Role of bandwidth in the quality of inversion of linear multirate systems with noise, Proc. 46th Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, Nov. 2012. 36. P. P. Vaidyanathan and Piya Pal, Why does direct-music on sparse-arrays work?, Proc. 47th Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, Nov. 2013. 37. P. P. Vaidyanathan and Piya Pal, The Farey dictionary for sparse representation of periodic signals, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc., Florence, Italy, May 2014.

BHASKAR D. RAO Education Ph.D M.S B.Tech Electrical Engineering, University of Southern California, Los Angeles (August 1983) Computer Engineering, University of Southern California, Los Angeles, (1981) Electronics and Electrical Communication Engineering, I.I.T. Kharagpur, India (1979) Professional Experience Professor, Department of Electrical and Computer Engineering, University of California, San Diego. (1995-present) Consultant, Qualcomm Inc., Fall 2000 Visiting Researcher, Speech technology department, Microsoft Research, (Summer 1997) Consultant, Speech Research Department, AT&T Bell Laboratories, Murray Hill, New Jersey, (Jan.-June, 1995). Visiting Associate Professor, Department of Electrical Engineering, Stanford University. (1989-1990) Awards and Honors 1. 2016 Signal Processing Society Technical Achievement Award for fundamental contributions to array processing and sparsity-based signal processing. 2. Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, 2015-2016 3. Director, Center for Wireless Communications, 2008-2011 4. Ericsson endowed chair in wireless access networks since May 2008. 5. IEEE Signal Processing Society Distinguished Lecturer for the term 1 January 2014 through 31 December 2015. 6. IEEE Fellow, 2000, for the statistical analysis of subspace algorithms for harmonic retrieval. 7. 2012 Signal Processing Society (SPS) Best Paper Award for the paper An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem, by David P. Wipf and Bhaskar D. Rao published in IEEE Transaction on Signal Processing, Volume: 55, No. 7, July 2007

8. 2008 Stephen O. Rice Prize Paper Award in the Field of Communication Systems for the paper Network Duality for Multiuser MIMO Beamforming Networks and Applications, by B. Song, R. L. Cruz and B. D. Rao that appeared in the IEEE Transactions on Communications, Vol. 55, No. 3, March 2007, pp. 618 630. (http://www.comsoc.org/ awards/rice.html) 9. Best student paper award at NIPS 2006 for D. Wipf for the paper Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization, by D.P.Wipf, R.R. Ramirez, J.A. Palmer, S. Makeig, and B.D. Rao in Advances in Neural Infor- mation Processing Systems, Dec. 2006 (http://nips.cc/conferenceinformation/paperawards) Selected Publications 1. D.P. Wipf, B.D. Rao, and S. Nagarajan, Latent Variable Bayesian Models for Promoting Sparsity, IEEE Transactions on Information Theory, 2011. 2. D. P. Wipf and B. D. Rao, An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem, IEEE Trans. on Signal Processing, July 2007. 3. S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, Sparse Solutions to Linear Inverse Problems with Multiple Measurement Vectors, IEEE Trans. on Signal Processing, 2005. 4. S. F. Cotter and B. D. Rao, Sparse Channel Estimation Via Matching Pursuit with Application to Equalization IEEE Trans. on Communications, Vol. 50, March 2002 5. I. F. Gorodnitsky and B. D. Rao, Sparse Signal Reconstruction from Limited Data Using FOCUSS: A Re-Weighted Norm Minimization Algorithm, IEEE Trans. On Signal Processing, Mar-1997, 6. J. C. Roh and B. D. Rao, Transmit Beamforming in Multiple-Antenna Systems with Finite Rate Feedback: A VQ-Based Approach, IEEE Trans. Information Theory. vol. 52, no. 3, pp. 1101-1112, Mar. 2006. 7. S. Shivappa, M. M. Trivedi, and B. D. Rao, Audio-visual Information Fusion In Human Computer Interfaces and Intelligent Environments: A Survey, Proceedings of the IEEE, October 2010. 8. S. Dharanipragada, U. H. Yapanel, and B. D. Rao, Robust Feature Extraction for Continuous Speech Recognition using the MVDR Spectrum Estimation Method, IEEE Trans. on Speech, Audio and Language Processing, pages 224-234, Jan. 2007. 9. A. D. Subramaniam, B. D. Rao, and W. R. Gardner, Low-Complexity Source Coding using Gaussian Mixture Models, Lattice Vector Quantization and Recursive Coding with Application to Speech Spectrum Quantization, IEEE Trans. on Speech and Audio Proc, 2006. 10. M. N. Murthi and B. D. Rao, All-Pole Modeling of Voiced Speech Base on the Minimum Variance Distortionless Response Spectrum, IEEE Trans. On Speech and Audio Processing, Page(s): 221-239, May 2000. 2

Curriculum Vitae (July 2017) Paul H. Siegel Distinguished Professor, Electrical and Computer Engineering Endowed Chair, Center for Magnetic Recording Research 9500 Gilman Drive, La Jolla, California 92093-0401 Telephone: (858) 534-6210 Fax: (858) 534-8059 E-mail: psiegel@ucsd.edu Biography: Paul H. Siegel received the S.B. and Ph.D. degrees in mathematics from the Massachusetts Institute of Technology (MIT), Cambridge, in 1975 and 1979, respectively. He held a Chaim Weizmann Postdoctoral Fellowship at the Courant Institute, New York University. He was with the IBM Research Division in San Jose, CA, from 1980 to 1995. He joined the faculty at the University of California, San Diego in July 1995, where he is currently Professor of Electrical and Computer Engineering in the Jacobs School of Engineering. He is affiliated with the Center for Magnetic Recording Research where he holds an endowed chair and served as Director from 2000 to 2011. His primary research interests lie in the areas of information theory and communications, particularly coding and modulation techniques, with applications to digital data storage and transmission. Prof. Siegel was a member of the Board of Governors of the IEEE Information Theory Society from 1991 to 1996 and again from 2009 to 2014. He served as Co-Guest Editor of the May 1991 Special Issue on Coding for Storage Devices of the IEEE Transactions on Information Theory. He served the same Transactions as Associate Editor for Coding Techniques from 1992 to 1995, and as Editor-in-Chief from July 2001 to July 2004. He was also Co-Guest Editor of the May/September 2001 two-part issue on The Turbo Principle: From Theory to Practice and the February 2016 issue on Recent Advances in Capacity Approaching Codes of the IEEE Journal on Selected Areas in Communications. Prof. Siegel was co-recipient, with R. Karabed, of the 1992 IEEE Information Theory Society Paper Award and shared the 1993 IEEE Communications Society Leonard G. Abraham Prize Paper Award with B. H. Marcus and J.K. Wolf. With J. B. Soriaga and H. D. Pfister, he received the 2007 Best Paper Award in Signal Processing and Coding for Data Storage from the Data Storage Technical Committee of the IEEE Communications Society. He was the 2015 Padovani Lecturer of the IEEE Information Theory Society. Prof. Siegel is a member of Phi Beta Kappa and a Fellow of the IEEE. He was elected to the National Academy of Engineering in 2008.

Paul H. Siegel 2 Personal Data Date of birth: September 12, 1953 Place of birth: Berkeley, California Citizenship: U.S.A. Marital status: Married, two children Home address: 6846 Via Valverde La Jolla, California 92037 Telephone: (858) 551-4041 Education 1971 1975 Massachusetts Institute of Technology, S.B. Mathematics Degree conferred: June 1975 1975 1979 Massachusetts Institute oftechnology, Ph.D. Mathematics Degree conferred: June 1979 Dissertation title: Witt spaces: a geometric cycle theory for ko-homology at odd primes Advisors: Prof. James R. Munkres, Prof. Edward Y. Miller, Prof. R. Mark Goresky Current Position 7/95 present University of California, San Diego Professor, Electrical and Computer Engineering Distinguished Professor, Electrical and Computer Engineering (7/11 present) Endowed Chair, Center for Magnetic Recording Research (7/07 present) Director, Center for Magnetic Recording Research (9/00 6/11) Teaching and Student Advising Courses taught: Advising: Linear Systems Fundamentals Algebraic Coding Probabilistic Coding Trellis-Coded Modulation Advanced Coding and Modulation for Digital Communications 25 Ph.D., 10 M.S. graduated.

Paul H. Siegel 3 Other Professional Experience 8/80 8/95 IBM Research Division, Almaden Research Center, San Jose, California 1/94 8/95 Mathematics and Related Computer Science Department Department Manager 7/84 12/93 Signal Processing and Coding Project Project Manager 8/80 6/84 Recording Channels Project Research Staff Member 9/89 8/90 University of California at San Diego Department of Electrical and Computer Engineering and Center for Magnetic Recording Research Visiting Associate Professor 9/79 6/80 Courant Institute of Mathematical Sciences, New York, New York Chaim Weizmann Postdoctoral Fellow 7/79 9/79 Lockheed Missiles and Space Company, Sunnyvale, California Summer Research Assistant Consulting Experience 9/97 5/98 Conexant Systems, Inc., San Diego, California 5/00 8/00 Seagate Technology, Irvine, California 9/98 8/08 Marvell Semiconductor, Inc., Santa Clara, California 9/04 6/12 Link-A-Media Devices, Inc., Santa Clara, California 11/09 3/10 Expert witness (prior art search) 10/15 1/16 Expert witness (prior art search, declaration preparation) Technical Advisory Boards 2004 2012 Link-A-Media Devices 2015 present OmniTier Storage

Paul H. Siegel 4 Other Academic Experience Santa Clara University (3/92 6/92). Adjunct Lecturer, Electrical Engineering and Computer Science. Co-taught Signal Processing and Coding for Data Transmission and Storage, Spring Quarter, 1992. University of California at San Diego (1/90 4/90). Visiting Associate Professor, Electrical and Computer Engineering. Taught Algebraic Coding, Winter Quarter, 1990. Santa Clara University (3/89 6/89). Adjunct Lecturer, Electrical Engineering and Computer Science. Co-taught Signal Processing and Coding for Data Transmission and Storage, Spring Quarter, 1989. Santa Clara University (1/86 4/86). Adjunct Lecturer, Electrical Engineering and Computer Science. Taught Constrained Coding for Storage Channels, Winter Quarter, 1986. University of California at Santa Cruz (1/85 4/85). Adjunct Lecturer, Computer and Information Sciences. Co-taught Information Theory, Winter Quarter, 1985. New York University (10/79 6/80). Instructor, Courant Institute of Mathematical Sciences. Taught Graduate Algebra Sequence, Fall and Spring Semesters, 1979-80. Other Teaching Experience Euler Institute for Discrete Mathematics and its Applications. Instructor, Inaugural Invited Mini-Course, Signal Processing and Coding for Digital Recording, March 21-25, 1994. International Institute for Engineering Education. Co-instructor, Signal Processing and Coding for Digital Recording Channels, Three-day Short Course, November 1992 1997. Politecnico di Torino. Instructor, Coding for Storage Systems, Lecture Series, May 2 26, 2006.

Paul H. Siegel 5 Professional Recognition and Awards Padovani Lecture, IEEE Information Theory Society, August 12, 2015 Lady Davis Visiting Professor, Faculty of Computer Science, Technion, Haifa, Israel (April June 2015). Viterbi Visiting Faculty Chair, Technion Computer Engineering (TCE) Center, Technion, Haifa, Israel (April June 2015). Graduate Teaching Award for 2009-2010, Department of Electrical and Computer Engineering, Jacobs School of Engineering, UC San Diego, March 2011. Outstanding Teacher of the Year Award, Jacobs School of Engineering, UC San Diego, June 2008. (Selected by Triton Engineering Student Council.) Member, National Academy of Engineering, February 2008. Endowed Chair, Center for Magnetic Recording Research, July 2007. Fellow of the IEEE, 1997. ( For contributions to signal processing and coding for storage systems ) 2007 IEEE Communications Society, Data Storage Technical Committee Best Paper Award in Signal Processing and Coding for Data Storage Determining and Approaching Achievable Rates of Binary Intersymbol Interference Channels Using Multistage Decoding (co-authored with H.D. Pfister and J.B. Soriaga). 1993 IEEE Communication Theory Society, Leonard G. Abraham Prize (Award in the Field of Communications Systems) Finite-State Modulation Codes for Data Storage (co-authored with B. Marcus and J.K. Wolf). 1992 IEEE Information Theory Society Best Paper Award Matched Spectral Null Codes for Partial Response Channels (co-authored with R. Karabed). Chaim Weizmann Post-Doctoral Fellowship (1979-1980). Phi Beta Kappa, M.I.T. Chapter, 1974.

Paul H. Siegel 6 IBM Awards Master Inventor, Almaden Research Center, June 1994 One of three designated at Almaden Research Center in 1994. Research Division Award, April 1991 Outstanding Government evaluation of IRAD project Dynamical Systems and Information Theory. Outstanding Innovation Award, June 1990. Enhanced 8/10 Matched Spectral Null Trellis Code for PRML Magnetic Recording Channels. Outstanding Technical Achievement Award, February 1983. Recording Channel Performance Model. Fifth Plateau Invention Achievement Award, September 1994. Fourth Plateau Invention Achievement Award, October 1993. Third Plateau Invention Achievement Award, February 1990. Second Plateau Invention Achievement Award, May 1988. First Plateau Invention Achievement Award, July 1986. Professional Society Activities Member, Board of Governors IEEE Information Theory Society (2012 15). IEEE Information Theory Society (2009 12). IEEE Information Theory Society (1994 97). IEEE Information Theory Society (1991 94). University Adviser, Executive Committee, Information Storage Industry Consortium (INSIC) (2007 11). Associate Editor for Coding Techniques IEEE Transactions on Information Theory (1992 95). Co-Guest Associate Editor Special Issue on Coding for Storage Devices, IEEE Transactions on Information Theory, May 1991. Guest Editor-in-Chief The Turbo Principle: From Theory to Practice, IEEE Journal on Selected Areas in Communications, May/September 2001. Editor-in-Chief IEEE Transactions on Information Theory, July 1, 2001 - June 30, 2004.

Paul H. Siegel 7 Associate Editor ACM Transactions on Storage (2012). Award Selection Committee Member IEEE Information Theory Society, 2001-2004, 2007, 2014, 2017. IEEE Communication Theory Society, Data Storage Technical Committee, Best Student Paper Prize, 2005. IEEE Communication Theory Society, Data Storage Technical Committee, Best Paper Prize, 2009. IEEE Hamming Medal Committee, 2009, 2010, 2011. Conference Co-Chair UCSD Information Theory and Applications (ITA) Center Workshop, La Jolla, CA, January 2008. Workshop on Theoretical Advances in Information Recording, DIMACS Special Focus on Computational Information Theory and Coding, Rutgers University, March 2004. (Co-organizer with E. Soljanin and Adriaan J. Wijngaarden, Lucent Technologies-Bell Labs, and Bane Vasic, University of Arizona.) 1995 IEEE Information Theory Workshop, Rydzyna, Poland. 1998 IEEE Information Theory Workshop, San Diego, California. Annual Non-Volatile Memories Workshop, UCSD, La Jolla, California, 2010-2017. Organizing Committee, 21st Magnetic Recording Conference (TMRC), 2010. Program Committee Member IEEE Information Theory Workshop, Jerusalem, Israel, April 2015. UCSD Information Theory and Applications (ITA) Center Workshop, La Jolla, CA, February 2007. International Advisory Committee, International Symposium on Information Theory and its Applications (ISITA) 2004, 2006, 2008. Technical Committee, INSIC Workshop on the Future of Data Storage Devices and Systems, La Jolla, CA, April 2004. The 11th Magnetic Recording Conference (TMRC 2000), Santa Clara, California, August 2000. IEEE International Symposium of Information Theory and its Applications, Victoria, British Columbia, September 1996.

Paul H. Siegel 8 SPIE International Symposium on Information, Communications and Computer Technology, Applications and Systems. Conference on Signal Processing and Coding for Information Storage, Philadelphia, October 1995. IEEE International Symposium on Information Theory, Budapest 1991, San Antonio 1993, Sorrento 2000, Washington, D.C. 2001, Seoul 2009, St. Petersburg 2011, Istanbul 2013, Barcelona 2016. IEEE International Magnetics Conference, Stockholm 1993. Session Organizer Sessions on Coding, UCSD Information Theory and Applications (ITA) Center Workshop, La Jolla, CA, 2006, 2009 2014. Session on Error Control and Modulation Coding, The 11th Magnetic Recording Conference (TMRC 2000), Santa Clara, California, August 2000. Session on Coding for Magnetic Storage, 1997 IEEE Information Theory Workshop, Longyearbyen, Norway, July 1997. Session on Coding, The 5th Annual Magnetic Recording Conference (TMRC) on Signal Processing, San Diego, August 1994. Special Session on Magnetic Recording, and Workshop on The Next Generation of Digital Recording Systems, at 1990 IEEE Global Telecommunications Conference (Globecom 90), San Diego, December 1990. Technical Reviewer ACM Transactions on Storage ETRI Journal European Transactions on Telecommunications IEEE Communications Letters IEEE Journal on Selected Areas in Communications IEEE Journal of Selected Topics in Quantum Electronics IEEE Transactions on Circuits and Systems, Part I IEEE Transactions on Communications IEEE Transactions on Information Theory IEEE Transactions on Magnetics

Paul H. Siegel 9 IEEE Transactions on Wireless Communications IEEE Journal on Selected Areas in Communications IEEE Journal of Selected Topics in Quantum Electronics Le Fonds québècois de recherche sur la nature et les technologies National Science Foundation National Sciences and Engineering Research Council of Canada Research Council of Norway South African National Research Foundation United States Israel Binational Science Foundation (Advisory Committee 2007, 2011, 2013) University of California, Microelectronics Innovation and Computer Research Opportunities (UC MICRO) Many major conferences in communications, information theory, and magnetic recording, including: Globecom, ICC, Intermag, ISIT, ISITA, International Symposium on Turbo Codes and Iterative Information Processing, TMRC. Society Member IEEE: Information Theory Society, Communications Society, Magnetics Society, Signal Processing Society. American Mathematical Society. Phi Beta Kappa, Massachusets Institute of Technology Chapter. Patents 18 U.S. Patents issued on signal processing and coding for data storage and transmission. 4,566,044 Direction-Constrained Ternary Codes Using Peak and Polarity Detection. (with G.G. Langdon, Jr.), January 21. 1986. 4,567,464 Fixed Rate Constrained Channel Code Generating and Recovery Method and Means Having Spectral Nulls for Pilot Signal Insertion. (with S.J. Todd), January 28, 1986.

Paul H. Siegel 10 4,786,890 Method and Apparatus for Implementing a PRML Code. (with B. Marcus, A. Patel), November 22, 1988. 4,870,414 Even Mark Modulation Coding Method. (with R. Karabed),September 26, 1989. 4,881,076 Encoding for Pit-Per-Transition Optical Data Recording. (with J. Ashley, M. Call), November 14, 1989. 4,882,583 Modified Sliding Block Code for Limiting Error Propagation. (with K. Dimitri, M. Hassner), November 21, 1989. 4,888,775 Trellis Codes for Partial Response Channels. (with R. Karabed), December 19, 1989. 4,888,779 Matched Spectral Null Trellis Codes for Partial Response Channels. (with R. Karabed), December 19, 1989. 4,949,196 Method and Apparatus for Asymmetrical RLL Coding. (with N. Davies, M. Hassner, T. Howell, R. Karabed), August 14, 1990. 5,095,484 Phase-Invariant Rate 8/10 Matched Spectral Null Code for PRML. (with R. Karabed), March 10, 1992. 5,280,489 Time-Varying Viterbi Detector for Control of Error Event Length. (with L. Fredrickson, R. Karabed, J. Rae, H. Thapar, R. Wood), January 18, 1994. 5,430,744 Method and Means for Detecting a Partial-Response Waveform Using a Modified Dynamic Programming Heuristic. (with G. Fettweis, R. Karabed, H. Thapar), July 4, 1995. 5,450,433 Method and Apparatus for Constructing Asymptotically Optimal Second Order DC-Free Channel Codes. (with A. Vardy), September 12, 1995. 5,497,384 Permuted Trellis Codes for Input Restricted Partial Response Channels. (with L. Fredrickson, R. Karabed, J. Rae, H. Thapar), March 5, 1996.

Paul H. Siegel 11 5,510,912 Method for Modulation of Multi-Dimensional Data in Holographic Storage. (with M. Blaum, G. Sincerbox, A. Vardy), April 23, 1996. 5,537,424 Matched Spectral Null Codes with Partitioned Systolic Trellis Structures. (with R. Karabed, J. Rae, H. Thapar), July 16, 1996. 5,548,600 Method and Means for Generating and Detecting Spectrally Constrained Coded Partial Response Waveforms Using a Time Varying Trellis Modified by Selective Output State Splitting. (with L. Fredrickson, R. Karabed, H. Thapar), August 20, 1996. 6,795,947 Parity Check Outer Code and Runlength Constrained Outer Code Usable with Parity Bits. (with M. Öberg), September 21, 2004. 8,977,936 Strong Single and Multiple Error Correcting WOM Codes, Coding Methods and Devices. (with E. Yaakobi, A. Vardy, and J. K. Wolf), March 10, 2015. 9,141,474 Efficient Two Write WOM Codes, Coding Methods and Devices. (with E. Yaakobi, A. Vardy, J. K. Wolf, and S. K. Kayser), September 22, 2015. Publications Book Chapters 1. L. Fredrickson, R. Karabed, P. Siegel, and H. Thapar, Decoding on a Finite State Transition Diagram While Avoiding a Sub-Diagram, DIMACS Series in Discrete Math. and Theoretical Comp. Science, Proc. Joint DIMACS/IEEE Workshop on Coding and Quantization, vol. 14, October 1992, pp. 149-151. 2. B. Marcus, R. Roth, and P. Siegel, Modulation Codes for Digital Data Storage, Proc. of Symposia Applied Mathematics, vol. 50, AMS Short Course Proceedings, Different Aspects of Coding Theory, R. Calderbank, ed., 1995, pp. 41 94. 3. B. Marcus, R. Roth, and P. Siegel, Constrained Systems and Coding for Recording Channels, Handbook of Coding Theory, Elsevier Scientific Publishers, 1998.(extended version of 2.)

Paul H. Siegel 12 4. K.A.S. Immink, P.H. Siegel, and J.K. Wolf, Codes for Digital Recorders, Information Theory: 50 Years of Discovery, S.W. McLaughlin and S. Verdu, eds., IEEE Press, 1999, pp. 216-255. 5. J.B. Soriaga, H.D. Pfister, and P.H. Siegel, On Approaching the Capacity of Finite- State Intersymbol Interference Channels, Information, Coding, and Mathematics, M. Blaum, P.G. Farrell, and H.C.A. van Tilborg, eds., Kluwer Academic Publishers, 2002, pp. 365-378. 6. N. Alić, G. C. Papen, L. B. Milstein, P. H. Siegel and Y. Fainman, Performance Bounds of MLSE of Intensity Modulated Fiber Optic Links, Optical Communication Theory Techniques, Enrico Forestieri, Ed., Springer Publishers, 2005, pp. 197 203. 7. P.H. Siegel, E. Soljanin, A.J. van Wijngaarden, and B. Vasić, Preface, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Advances in Information Recording, vol. 73, P.H. Siegel, E. Soljanin, A.J. van Wijngaarden, and B. Vasić, Eds. American Mathematical Society, 2008, pp. ix-xii. 8. N. Kashyap and P.H. Siegel, Ghostbusting: Coding for Optical Communications, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Advances in Information Recording, vol. 73, February 2008, pp. 147 164. 9. M.-P. Béal, J. Berstel, B.H. Marcus, D. Perrin, C. Reutenauer, and P.H. Siegel, Variable length codes and finite automata, Ch. 12 in Selected Topics in Information and Coding Theory, Series on Coding Theory and Cryptology, vol. 7, I. Woungang, S. Misra, S.C. Misra, Eds. World Scientific Publishing Co. Pte. Ltd., 2010, pp. 505-584. Journal Publications 1. P. Siegel, Applications of a Peak Detection Channel Model, IEEE Trans. Magn., vol. MAG-18, No. 6, pp. 1250 1252, November 1982. 2. P. Siegel, Witt Spaces: A Geometric Cycle Theory for ko-homology at Odd Primes, Am. J. Math., vol. 105, No. 5, pp.1067 1105, October 1983. 3. M. Goresky and P. Siegel, Linking Pairings on Singular Spaces, Comm. Math. Helv., vol. 58, No.1, pp. 96 110, 1983. 4. P. Siegel and B. Marcus, Worst Case Code Patterns for Magnetic Buried Servos, IEEE Trans. Magn., vol. MAG-20, No. 5, pp. 906 908, September 1984. 5. P. Siegel, Recording Codes for Digital Magnetic Storage, IEEE Trans. Magn., vol. MAG-21, No. 5, pp. 1344 1349, September 1985.

Paul H. Siegel 13 6. B. Marcus and P. Siegel, On Codes with Spectral Nulls at Rational Submultiples of the Symbol Frequency, IEEE Trans. Inform. Th., vol. 33, No. 4, pp. 557 568, July 1987. 7. J. Ashley and P. Siegel, A Note on the Shannon Capacity of Run-Length-Limited Codes, IEEE Trans. Inform. Th., vol. 33, No. 4, pp. 601 605, July 1987. (See also J. Ashley, M. Hilden, P. Perry, and P. Siegel, Correction to A Note on the Shannon Capacity of Run-Length-Limited Codes, IEEE Trans. Inform. Th., vol. 39, No. 3, pp. 1110 1112, May 1993.) 8. A. Gallopoulos, C. Heegard, and P. Siegel, The Power Spectrum of Run-Length- Limited Codes, IEEE Trans. Commun., vol. 37, no. 9, pp. 906 917, September 1989. 9. A.R. Calderbank, P. Siegel, and J. K. Wolf, Introduction to the Special Issue (on Coding for Storage Devices), IEEE Trans. Inform. Th., vol. 37, no. 3, pt. II, pp. 709 710, May 1991. 10. C. Heegard, B.H. Marcus, and P.H. Siegel, Variable Length State Splitting with Applications to Average Runlength Constrained (ARC) Codes, IEEE Trans. Inform. Th., vol. 37, no. 3, pt. II, pp. 759 777, May 1991. 11. R. Karabed and P. Siegel, Matched Spectral Null Codes for Partial Response Channels, IEEE Trans. Inform. Th., vol. 37, no. 3, pt. II, pp. 818 855, May 1991. 12. P. Siegel and J.K. Wolf, Modulation and Coding for Information Storage, IEEE Commun. Magazine, vol. 29, no. 12, pp. 68 86, December 1991. 13. B. Shung, P. Siegel, H. Thapar, R. Karabed, A 30 MHz Trellis Codec Chip for Partial- Response Channels, IEEE Journal of Solid-State Circuits, Special Issue on Analog and Signal Processsing Circuits, vol. 26, no. 12, pp. 1981-1987, December 1991. 14. B. Marcus, P. Siegel, and J.K. Wolf, Finite-State Modulation Codes for Data Storage, IEEE Journal Selec. Areas Commun., vol. 10, no. 1, pp. 5 37, January 1992. 15. P. Siegel, Review of Finite Fields for Computer Scientists and Engineers, by R. J. McEliece, IEEE Trans. Inform. Th., vol. 38, no. 1, January 1992. 16. H.K. Thapar, J. Rae, C.B. Shung, R. Karabed, and P.H. Siegel, Performance Evaluation of a Rate 8/10 Matched Spectral Null Code for Class-4 Partial Response, IEEE Trans. Magn., vol. 28, no. 5, pp. 2884 2889, September 1992. 17. C. Shung, H.-D. Lin, R. Cypher, P. Siegel, and H. Thapar, Area-Efficient Architectures for the Viterbi Algorithm Part I: Theory and Part II: Applications, IEEE Trans. Commun., pp. 636 644, April 1993 and pp. 802 807, May 1993.

Paul H. Siegel 14 18. H. Thapar, C. Shung, J. Rae, R. Karabed, and P. Siegel, Real-Time Recording Results for a Trellis-Coded Partial Response (TCPR) System, IEEE Trans. Magn., vol. 29, no. 6, pp. 4009 4011, November 1993. 19. R. Roth and P. Siegel, Lee-Metric BCH Codes and Their Application to Constrained and Partial-Response Channels, IEEE Trans. Inform. Th., pp. 1083 1096, July 1994. 20. R. Roth, P. Siegel, and A. Vardy, High-Order Spectral-Null Codes: Constructions and Bounds, IEEE Trans. Inform. Th., pp. 1828 1840, November 1994. 21. L. Fredrickson, R. Karabed, P. Siegel, H. Thapar, and R. Wood, Improved Trellis Coding for Partial-Response Channels, IEEE Trans. Magn., pp. 1141 1148, March 1995. 22. J. Rae, G. Christiansen, S.-M. Shih, H. Thapar, R. Karabed, and P. Siegel, Design and Performance of a VLSI 120 Mb/s Trellis-Coded Partial-Response Channel, IEEE Trans. Magn., pp. 1208 1214, March 1995. 23. A. Vardy, M. Blaum, P.H. Siegel, and G.T. Sincerbox, Conservative Arrays: Multidimensional Modulation Codes for Holographic Recording, IEEE Trans. Inform. Th., pp. 227 230, January 1996. 24. J.J. Ashley, R. Karabed, and P.H. Siegel, Complexity and Sliding-Block Decodability, IEEE Trans. Inform. Th., Special Issue on Codes and Complexity, November 1996. 25. B.E. Moision, P.H. Siegel, and E. Soljanin, Distance-enhancing codes for digital recording, IEEE Trans. Magn., vol. 34, no. 1, pp. 69 74, Jan. 1998. 26. K.A.S. Immink, P.H. Siegel, J.K. Wolf, Codes for Digital Recorders, IEEE Trans. Inform. Theory., Special Commemorative Issue, pp. 2260 2299, October 1998. 27. A. Vityaev and P.H. Siegel, Improved Estimates of Viterbi Detector Path Metric Differences, IEEE Trans. Commun., pp. 1549 1554, December 1998. 28. S.A. Atlekar, M. Berggren, B.E. Moision, P.H. Siegel, and J.K. Wolf, Error Event Characterization on Partial Response Channels, IEEE Trans. Inform. Th., pp. 241 247, January 1999. 29. R. Karabed, P.H. Siegel, and E. Soljanin, Constrained Coding for Binary Channels with High Intersymbol Interference, IEEE Trans. Inform. Th., vol. 45, no. 6, pp. 1777 1797, September 1999. 30. T. Souvignier, M. Öberg, P.H. Siegel, R.E. Swanson, and J.K. Wolf, Turbo Decoding for Partial Response Channels, IEEE Trans. Commun., vol. 48, no. 8, pp. 1297 1308, August 2000.

Paul H. Siegel 15 31. M. Öberg and P. H. Siegel, Performance Analysis of Turbo-Equalized Partial- Response Channels, IEEE Trans. Commun., vol. 49, no. 3, pp. 436 444, March 2001 32. B.E. Moision, A. Orlitsky, and P.H. Siegel, On Codes That Avoid Specified Differences, IEEE Trans. Inform. Th., vol. 47, no. 1, pp. 433 442, January 2001. 33. R.M. Roth, P.H. Siegel, J.K. Wolf, Efficient Coding for the Hard-Square Model, IEEE Trans. Inform. Th., vol. 47, no. 3, pp. 1166 1176, March 2001. 34. P.H. Siegel, D. Divsalar, E. Eleftheriou, J. Hagenauer, D. Rowitch, Guest editorial: The Turbo Principle: From Theory to Practice, I, IEEE Journal Selec. Areas Commun., vol. 19, no. 5, pp. 793 799, May 2001. 35. J. Hou, L. Milstein, and P.H. Siegel, Performance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels, IEEE Journal Selec. Areas Commun., vol. 19, no. 5, pp. 924 934, May 2001. 36. K. Tang, L.B. Milstein, and P.H. Siegel, A Comparison of Long Versus Short Spreading Sequences in Coded Asynchronous DS-CDMA Systems, IEEE Journal Selec. Areas Commun., vol. 19, no. 8, pp. 1614 1624, August 2001. 37. P.H. Siegel, D. Divsalar, E. Eleftheriou, J. Hagenauer, D. Rowitch, Guest editorial: The Turbo Principle: From Theory to Practice, II, IEEE Journal Selec. Areas Commun., vol. 19, no. 9, pp. 1659 1661, September 2001. 38. K. Tang, L. Milstein, and P.H. Siegel, Combined MMSE Interference Suppression and Turbo Coding for a Coherent DS-CDMA System, IEEE Journal Selec. Areas Commun., vol. 19, no. 9, pp. 1793 1803. September 2001. 39. X.-W. Wu and P.H. Siegel, Efficient List Decoding of Algebraic Geometric Codes Beyond the Error Correction Bound, IEEE Trans. Inform. Th., vol. 47, no. 6, pp. 2579 2587, September 2001. 40. B.M. Kurkoski, P.H. Siegel, and J.K. Wolf, Joint Message-Passing Decoding of LDPC Codes and Partial-Response Channels, IEEE Trans. Inform. Th., vol. 48, no. 6, pp. 1410 1422, June 2002. (See also Correction, IEEE Trans. Inform. Th., vol. 49, no. 8, pp. 2076, August 2003. 41. H. D. Pfister and P. H. Siegel, The Serial Concatenation of Rate-1 Codes Through Uniform Random Interleavers, IEEE Trans. Inform. Th., vol. 49, no. 6, pp. 14251-438, June 2003. 42. K. Tang, L. B. Milstein, and P.H. Siegel, MLSE Receiver for Direct- Sequence Spread- Spectrum Systems on a Multipath Fading Channel, IEEE Trans. Commun., vol. 51, no. 7, pp. 1173-1184, July 2003.

Paul H. Siegel 16 43. J. Hou, P. H. Siegel, L. B. Milstein, and H. D. Pfister, Capacity- Approaching Bandwidth-Efficient Coded Modulation Schemes Based on Low-Density Parity-Check Codes, IEEE Trans. Inform. Th., vol. 49, no. 9, pp. 2141-2155, September 2003. 44. J.B. Soriaga, H.D. Pfister, and P.H. Siegel, On the Low Rate Shannon Limit for Binary Intersymbol Interference Channels, IEEE Trans. Commun., vol. 51, no. 12, pp. 1962 1964, December 2003. 45. N. Kashyap and P.H. Siegel, Equalities among Capacities of (d,k) Constrained Systems, SIAM J. Discr. Math., vol. 17, no. 2, pp. 276 297, 2003. 46. S. Halevy, J. Chen; R.M. Roth, P.H. Siegel, J.K. Wolf, Improved Bit-Stuffing Bounds on Two-Dimensional Constraints, IEEE Trans. Inform. Th., vol. 50, no. 5, pp. 824 838, May 2004. 47. N. Kashyap and P.H. Siegel, Sliding-Block Decodable Encoders Between (d, k) Runlength-Limited Constraints of Equal Capacity, IEEE Trans. Inform. Th., vol. 50, no. 6, pp. 1327 1331, June 2004. 48. M.N. Marrow, M.K. Cheng, P.H. Siegel, and J.K. Wolf, A Fast Microtrack Simulator for High-Density Perpendicular Recording, IEEE Trans. Magn., vol. 40, no. 4, pp. 3117 3119, July 2004. 49. A.P. des Rosiers and P.H. Siegel, On Performance Bounds for Space-Time Codes on Fading Channels, IEEE Trans. Commun., vol. 52, no. 10, pp. 1688 1697, October 2004. 50. H.M. Tullberg and P.H. Siegel, Serial Concatenated TCM with an Inner Accumulate Code part I: Maximum-Likelihood Analysis, IEEE Trans. Commun., vol. 53, no. 1, pp. 64 73, January 2005. 51. H.M. Tullberg and P.H. Siegel, Serial Concatenated TCM with an Inner Accumulate Code part II: Density Evolution Analysis, IEEE Trans. Commun., vol. 53, no. 2, pp. 252 262, February 2005. 52. J. Hou, P.H. Siegel, and L.B Milstein, Design of Multi-Input Multi-Output Systems Based on Low-Density Parity-Check Codes, IEEE Trans. Commun., vol. 53, no. 4, pp. 601611, April 2005. 53. Y. Zhang, L.B. Milstein, and P.H. Siegel, Tradeoff Between Diversity Gain and Interference Suppression in a MIMO MC-CDMA System, IEEE Trans. Commun., vol. 53, no. 4, pp. 623 631, April 2005. 54. S. Aviran, P.H. Siegel, and J.K. Wolf, An Improvement to the Bit Stuffing Algorithm, IEEE Trans. Inform. Th., vol. 51, no. 8, pp. 2885 2891, August 2005.

Paul H. Siegel 17 55. B. Kurkoski, P.H. Siegel, and J.K. Wolf, Soft-Output Detector for Partial-Response Channels Using Vector Quantization, IEEE Trans. Magn., vol. 41, no. 10, pp. 2989 2991, October 2005. 56. S. Aviran, P.H. Siegel, and J.K. Wolf, Noise-Predictive Turbo Equalization for Partial- Response Channels, IEEE Trans. Magn., vol. 41, no. 10, pp. 2959 2961, October 2005. 57. N. Kashyap, P.H. Siegel and A. Vardy, An Application of Ramsey Theory to Coding for the Optical Channel, SIAM J. Discr. Math., vol. 19, no. 4, pp. 921 937, December 2005. 58. N. Kashyap, P.H. Siegel and A. Vardy, Coding for the Optical Channel the Ghost Pulse Constraint, IEEE Trans. Inform. Th., vol. 52, no. 1, pp. 64 77, January 2006. 59. J. Chen and P.H. Siegel, On the Symmetric Information Rate of Two-Dimensional Finite State ISI Channels, IEEE Trans. Inform. Th., vol. 52, no. 1, pp. 227 236, January 2006. 60. P. Chaichanavong and P.H. Siegel, A Tensor-Product Parity Code for Magnetic Recording, IEEE Trans. Magn., vol. 42, no. 2, pt. 2, pp. 350 352, February 2006. 61. P. Chaichanavong and P.H. Siegel, Tensor-Product Parity Codes: Combination with Constrained Codes and Application to Perpendicular Recording, IEEE Trans. Magn., vol. 42, no. 2, pt. 1, pp. 214 219, February 2006. 62. J. Han, P. Lee, and P.H. Siegel, On the Probability of Undetected Error for Over- Extended Reed-Solomon Codes, IEEE Trans. Inform. Th., vol. 52, no. 8, pp. 3662 3669, August 2006. 63. P. Chaichanavong, H.N. Bertram, and P.H. Siegel, Design Parameter Optimization for Perpendicular Magnetic Recording Systems, IEEE Trans. Magn., vol. 42, no. 10, pp. 2549 2554, October 2006. 64. M.H. Taghavi N., G.C. Papen, and P.H. Siegel, On the Multiuser Capacity of WDM in a Nonlinear Optical Fiber: Coherent Communication, IEEE Trans. Inform. Th., vol. 52, no. 11, pp. 5008 5022, November 2006. 65. P.H. Siegel, Book Review: Codes for Mass Data Storage Systems (Second Edition), by K.A. Schouhamer Immink, IEEE Trans. Inform. Th., vol. 52, no. 12, pp. 5614 5616, December 2006. 66. J. Han and P.H. Siegel, Improved Upper Bounds on Stopping Redundancy, IEEE Trans. Inform. Th., vol. 53, no. 1, pp. 90 104, January 2007. 67. Z. Wu, P.H. Siegel, H.N. Bertram, and J.K. Wolf, Design Curves and Information Theoretic Limits for Perpendicular Recording Systems, IEEE Trans. Magn., vol. 43, no. 2, pp. 721 726, February 2007.

Paul H. Siegel 18 68. J.B. Soriaga, H.D. Pfister, and P.H. Siegel, Determining and Approaching Achievable Rates of Binary Intersymbol Interference Channels using Multistage Decoding, IEEE Trans. Inform. Th., vol. 53, no. 4, pp. 1416 1429, April 2007. 69. B.E. Moision, A. Orlitsky, and P.H. Siegel, On Codes with Local Joint Constraints, Lin. Alg. Appl., vol. 422, iss. 2-3, pp. 442 454, April 15, 2007. 70. S. Karakulak, P.H. Siegel, J.K. Wolf, and H.N. Bertram, A New Read Channel Model for Patterned Media Storage, IEEE Trans. Magn., vol. 44, no. 1, pt. 2, pp. 193 197, January 2008. 71. H.D. Pfister and P.H. Siegel, Joint Iterative Decoding of LDPC Codes for Channels with Memory and Erasure Noise, IEEE Journal Selec. Areas Commun., vol. 26, no. 2, pp. 320 337, February 2008. 72. S. Aviran, P.H. Siegel, and J.K. Wolf, Optimal Parsing Trees for Run-Length Coding of Biased Data, IEEE Trans. Inform. Th., vol. 54, no. 2, pp. 841 849, February 2008. 73. J. Chen and P.H. Siegel, Markov Processes Asymptotically Achieve the Capacity of Finite-State Intersymbol Interference Channels, IEEE Trans. Inform. Th., vol. 54, no. 3, pp. 1295 1303, March 2008. 74. J. Han, P.H. Siegel, and A. Vardy, Improved Probabilistic Bounds on Stopping Redundancy, IEEE Trans. Inform. Th., vol. 54, no. 4, pp. 1749 1753, April 2008. 75. Z. Wu, P.H. Siegel, J.K. Wolf, and H.N. Bertram, Improved Mean-Adjusted Pattern- Dependent Noise Prediction for Perpendicular Recording Channels With Nonlinear Transition Shift, IEEE Trans. Magn., vol. 44, no. 11, pp. 3761 3764, November 2008. 76. M.H. Taghavi N. and P.H. Siegel, Adaptive Methods for Linear Programming Decoding, IEEE Trans. Inform. Th., vol. 54, no. 12, pp. 5396 5410, December 2008. 77. I. Demirkan, P.H. Siegel, and J.K. Wolf, Error Event Characterization on 2-D ISI Channels, IEEE Trans. Inform. Th., vol. 55, no. 3, pp. 1146 1152, March 2009. 78. J. Han, P.H. Siegel, and R.M. Roth, Single-Exclusion Number and the Stopping Redundancy of MDS Codes, IEEE Trans. Inform. Th., vol. 55, no. 9, pp. 4155 4166, September 2009. 79. Z. Wu, P.H. Siegel, J.K. Wolf, and H.N. Bertram, Analysis of Nonlinear Transition Shift and Write Precompensation in Perpendicular Recording Systems, IEEE Journal Selec. Areas Commun., vol. 28, no.2, pp.158 166, February 2010. 80. S. Karakulak, P.H. Siegel, and J.K. Wolf, A Parametric Study of Inter-Track Interference in Bit Patterned Media Recording, IEEE Trans. Magn., vol.46, no.3, pp.819 824, March 2010.

Paul H. Siegel 19 81. S. Karakulak, P.H. Siegel, and J.K. Wolf, Joint Track Equalization and Detection for Bit Patterned Media Recording, IEEE Trans. Magn., vol. 46, no. 9, pp. 3639 3647, September 2010. 82. A. Jiang, R. Mateescu, E. Yaakobi, J. Bruck, P.H. Siegel, A. Vardy, and J.K. Wolf, Storage Coding for Wear Leveling in Flash Memories, IEEE Trans. Inform. Theory, vol. 56, no. 10, no. pp. 5290-5299, October 2010. 83. A.R. Iyengar, P.H. Siegel, and J.K. Wolf, Write Channel Model for Bit-Patterned Media Recording, IEEE Trans. Magn., vol. 47, no. 1, pp. 35 45, January 2011. 84. M.H. Taghavi and P.H. Siegel, Graph-Based Decoding in the Presence of ISI, IEEE Trans. Inform. Theory, vol. 57, no. 4, pp. 2188 2202, April 2011. 85. M.-P. Béal, M. Crochemore, B.E. Moision, and P.H. Siegel, Periodic-Finite-Type Shift Spaces, IEEE Trans. Inform. Theory, vol. 57, no. 6, pp. 3677 3691, June 2011. 86. M.H. Taghavi, A. Shokrollahi, and P.H. Siegel, Efficient Implementation of Linear Programming Decoding, IEEE Trans. Inform. Theory, vol. 57, no. 9, pp. 5960 5982, September 2011. 87. E. Yaakobi, P.H. Siegel, A. Vardy, and J.K. Wolf, Multiple Error-Correcting WOM- Codes, IEEE Trans. Inform. Theory, vol. 58, no. 4, pp. 2220 2230, April 2012. 88. A.R. Iyengar, M. Papaleo, P.H. Siegel, J.K. Wolf, A. Vanelli-Coralli, G.E. Corazza, Windowed Decoding of Protograph-based LDPC Convolutional Codes over Erasure Channels, IEEE Trans. Inform. Theory, vol. 58, no. 4, pp. 2303 2320, April 2012. 89. E. Yaakobi, S. Kayser, P.H. Siegel, A. Vardy, and J.K. Wolf, Codes for Write-Once Memories, IEEE Trans. Inform. Theory, vol. 58, no. 9, pp. 5985 5999, September 2012. 90. X. Zhang and P.H. Siegel, Adaptive Cut Generation Algorithm for Improved Linear Programming Decoding of Binary Linear Codes, IEEE Trans. Inform. Theory, vol. 58, no. 10, pp. 6581 6594, October 2012. 91. A.R. Iyengar, P.H. Siegel, R.L. Urbanke, and J.K. Wolf, Windowed Decoding of Spatially Coupled Codes, IEEE Trans. Inform. Theory, vol. 59, no. 4, pp. 2277 2292, April 2013. 92. B.K. Butler and P.H. Siegel, Bounds on the Minimum Distance of Punctured Quasi- Cyclic LDPC Codes, IEEE Trans. Inform. Theory, vol. 59, no. 7, pp. 4584 4597, July 2013. 93. M. Qin, E. Yaakobi, and P.H. Siegel, Time-Space Constrained Codes for Phase- Change Memories, IEEE Trans. Inform. Theory, vol. 59, no. 8, pp. 5102 5114, August 2013.

Paul H. Siegel 20 94. B.K. Butler and P.H. Siegel, Sharp Bounds on the Spectral Radius of Nonnegative Matrices and Digraphs, Lin. Alg. Appl., vol. 439, no. 5, pp. 1468 1478, September 2013. 95. X. Zhang and P.H. Siegel, Quantized Iterative Message Passing Decoders with Low Error Floor for LDPC Codes, IEEE Trans. Commun., vol. 62, no. 1, pp. 1 14, January 2014. 96. E. Yaakobi, H. Mahdavifar, P. H. Siegel, A. Vardy, and J. K. Wolf, Rewriting Codes for Flash Memories, IEEE Trans. Inform. Theory, vol. 60, no. 2, pp. 964 975, February 2014. 97. M. Qin, E. Yaakobi, and P. H. Siegel, Optimized Cell Programming for Flash Memories with Quantizers, IEEE Trans. Inform. Theory, vol. 60, no. 5, pp. 2780 2795, May 2014. 98. M. Qin, E. Yaakobi, and P.H. Siegel, Constrained Codes that Mitigate Inter-Cell Interference in Read/Write Cycles for Flash Memories, IEEE Journal Selec. Areas Commun., vol. 32, no. 5, pp. 836 846, May 2014. 99. A. Bhatia, M. Qin, A.R. Iyengar, B.M. Kurkoski, and P.H. Siegel, Lattice-Based WOM Codes for Multilevel Flash Memories, IEEE Journal Selec. Areas Commun., vol. 32, no. 5, pp. 933 945, May 2014. 100. A. Bhatia, S. Yang, and P. H. Siegel, Precoding Mapping Optimization for Magnetic Recording Channels, IEEE Trans. Magn., vol. 50, no. 11, paper 3102304, Nov. 2014. 101. B. K. Butler and P. H. Siegel, Error Floor Approximation for LDPC Codes in the AWGN Channel, IEEE Trans. Inform. Theory, vol. 60, no. 12, pp. 7416 7441, December 2014. 102. B. Peleato, R. Agarwal, J. M. Cioffi, M. Qin, and P. H. Siegel, Adaptive Read Thresholds for NAND Flash, IEEE Trans. Commun., vol. 63, no. 9, pp. 3069 3081, September 2015. 103. B. Fan, H. K. Thapar, and P. H. Siegel, Multihead Multitrack Detection With Reduced-State Sequence Estimation, IEEE Trans. Magn., vol. 51, no. 11, paper 3001404, November 2015. 104. A. R. Iyengar, P. H. Siegel, and J. K. Wolf, On the Capacity of Channels With Timing Synchronization Errors, IEEE Trans. Inform. Theory, vol. 62, no. 2, pp. 793 810, Feburary 2016. 105. E. Arkan, D. J. Costello, J. Kliewer, M. Lentmaier, P. H. Siegel, R. Urbanke, and M. Pursley, Guest Editorial Recent Advances in Capacity Approaching Codes, IEEE Journal Selec. Areas Commun., vol. 34, no. 2, pp. 205 208, Feburary 2016.

Paul H. Siegel 21 106. E. Yaakobi, J. Bruck, and P. H. Siegel, Constructions and Decoding of Cyclic Codes Over b-symbol Read Channels, IEEE Trans. Inform. Theory, vol. 62, no. 4, pp. 1541 1551, April 2016. 107. V. Taranalli, H. Uchikawa, and P. H. Siegel, Channel Models for Multi-Level Cell Flash Memories Based on Empirical Error Analysis, IEEE Trans. Commun., vol. 64, no. 8, pp. 3169 3181, August 2016. 108. V. Taranalli, H. Uchikawa, and P. H. Siegel, On the Capacity of the Beta-Binomial Channel Model for Multi-Level Cell Flash Memories, IEEE Journal Selec. Areas Commun., vol. 34, no. 9, pp. 2312 2324, September 2016. 109. P. Huang, P. H. Siegel, and E. Yaakobi, Performance of Multilevel Flash Memories with Different Binary Labelings: a Multi-user Perspective, IEEE Journal Selec. Areas Commun., vol. 34, no. 9, pp. 2336 2353, September 2016. 110. P. Huang, E. Yaakobi, H. Uchikawa, and P. H. Siegel, Binary Linear Locally Repairable Codes, IEEE Trans. Inform. Theory, vol. 62, no. 11, pp. 6268 6283, November 2016. 111. B.g Fan, H. K. Thapar and Paul H. Siegel, Multihead Multitrack Detection for Next Generation Magnetic Recording, Part I: Weighted Sum Subtract Joint Detection with ITI Estimation, IEEE Trans. Commun., vol. 65, no. 4, pp. 1635 1648, April 2017. 112. B. Fan, H. K. Thapar and P. H. Siegel, Multihead Multitrack Detection for Next Generation Magnetic Recording, Part II: Complexity Reduction Algorithms and Performance Analysis, IEEE Trans. Commun., vol. 65, no. 4, pp. 1649 1661, April 2017. 113. M. Qin, J. Guo, A. Bhatia, A. Guilln i Fbregas, and P. H. Siegel, Polar Code Constructions Based on LLR Evolution, IEEE Commun. Letters, vol. 21, no. 6, pp. 1221 1224, June 2017. 114. 15. S. Buzaglo and P. H. Siegel, Row-by-Row Coding Schemes for Inter-Cell Interference in Flash Memory, IEEE Trans. Commun., to appear. Refereed Conference Publications 1. R. Karabed and P. Siegel, Matched Spectral Null Codes for Partial Response Channels, Parts I and II, Abstracts IEEE Int. Symp. on Inform. Th., Kobe, Japan, June 1988, pp. 142 143. 2. B. Marcus and P. Siegel, Constrained Codes for Partial Response Channels, Proc. IEEE Int. Workshop Inform. Th., Beijing, July 1988, pp. DI1.1 DI1.4. 3. R. Karabed and P. Siegel, Even Mark Modulation for Optical Recording, Proc. IEEE Int. Conf. Commun., Boston, June 1989, pp. 1628 1632.

Paul H. Siegel 22 4. D. Rugar and P. Siegel, Recording Results and Coding Considerations for the Resonant Bias Coil Overwrite Technique, Optical Data Storage Topical Meeting, G. R. Knight, C.N. Kurtz, Eds., Proc. SPIE 1078, 1989, pp. 265 270. 5. C.Shung, P. Siegel, G. Ungerboeck, and H. Thapar, VLSI Architectures for Metric Normalization in the Viterbi Algorithm, Proc. IEEE Int. Conf. Commun., vol. 3., Atlanta, April 1990, pp. 1723 1728. 6. C. Shung, H.-D. Lin, P. Siegel, and H. Thapar, Area-Efficient Architectures for the Viterbi Algorithm, Proc. IEEE Global Telecommun. Conf., vol. 3, San Diego, December 1990, pp. 1787 1793. 7. P. Siegel, C. Shung, T. Howell, and H. Thapar, Exact Bounds for Viterbi Detector Path Metric Differences, Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Toronto, May 1991, pp. 1093 1096. 8. R. Karabed and P.H. Siegel, A 100% Efficient Sliding-Block Code for the Charge- Constrained, Runlength-Limited Channel with Parameters (d, k; c) = (1, 3; 3), Proc. IEEE Int. Symp. Inform. Th., Budapest, Hungary, June 1991, p. 229. 9. H. Thapar, J. Rae, C. Shung, R. Karabed, and P. Siegel, Experimental Evaluation of a Rate 8/10 Matched Spectral Null Code for PRML, Proc. IEEE Int. Symp. Sign., Syst., and Electr., Paris, France, September 1992, pp. 805 808. 10. R. Calderbank, P. Fishburn, and P. Siegel, State-Space Characterization of Viterbi Detector Path Metric Differences, Conf. Rec. Twenty-Sixth Asilomar Conf. on Signals, Systems, and Computers, vol. 2, Pacific Grove, October 1992, pp. 940 944. 11. R. Roth and P. Siegel, A Family of BCH Codes for the Lee Metric, Proc. 30th Annual Allerton Conf. on Communication, Control, and Computing, Urbana-Champaign, pp. 1 10, September 1992. Also, Proc. IEEE Int. Symp. Inform. Th., San Antonio, January 1993, p. 35. 12. A. Vardy, M. Blaum, P.H. Siegel, and G.T. Sincerbox, Two-dimensional modulation codes for holographic storage, Proc. 32nd Annual Allerton Conf. on Communication, Control, and Computing, Urbana-Champaign, September 1994, pp. 200 209. 13. R. Karabed and P. Siegel, Coding for higher-order partial-response channels, in Coding and Signal Processing for Information Storage, Mysore R. Raghuveer, Soheil A. Dianat, Steven W. McLaughlin, Martin Hassner, Editors, Proc. SPIE 2605, pp. 115 126, October 1995. 14. G. Fettweis, R. Karabed, P.H.Siegel, and H.K. Thapar, Reduced-Complexity Viterbi Detector Architectures for Partial Response Signalling, Proc. IEEE Global Telecommun. Conf., vol. 1, Singapore, December 1995, pp. 559-563. 15. A. Vityaev and P.H. Siegel, Improved Estimates of Viterbi Detector Path Metric Differences, Proc. IEEE Global Telecommun. Conf., London, December 1996.

Paul H. Siegel 23 16. S. Atlekar, M. Berggren, B. Moision, P. Siegel, and J.K. Wolf, Error Event Characterization on Partial Response Channels, Proc. IEEE Int. Symp. Inform. Th., Ulm, Germany, June 1997, p. 461. 17. M. Öberg and P. Siegel, Lowering the Error Floor of Turbo Codes, Proc. IEEE Int. Symp. Turbo Codes, Breste, France, September 1997. 18. M. Öberg and P. Siegel, The Effect of Puncturing on Turbo Code Performance, Proc. IEEE Int. Symp. Turbo Codes, Breste, France, September 1997. 19. M. Öberg and P.Siegel, Application of Distance Spectrum Analysis to the Improvement of Turbo Code Performance, Proc. 35-th Annual Allerton Conf. Commun., Control, and Computing, pp. 701 710, September 1997. (invited) 20. P. H. Siegel and J. K. Wolf, Bit-Stuffing Bounds on the Capacity of 2-Dimensional Constrained Arrays, Proc. IEEE Int. Symp. Inform. Theory, Cambridge, Massachusetts, p. 323, August 1998. 21. B. E. Moision, P. H. Siegel, and E. Soljanin, Error Event Characterization and Coding for the Equalized Lorentzian Channel, Proc. IEEE Int. Symp. Inform. Theory, Cambridge, Massachusetts, p. 77, August 1998. 22. M. Öberg and P. H. Siegel, Performance Analysis of Turbo-Equalized Dicode Partial- Response Channel, Proc. 36th Allerton Conference on Communication, Control and Computing, Monticello, Illinois, pp. 230 239, September 1998. 23. J. K. Wolf and P.H. Siegel, On Two-Dimensional Arrays and Crossword Puzzles, Proc. 36th Allerton Conference on Communication, Control and Computing, Monticello, Illinois, pp. 366 371, September 1998. 24. S. Halter, M. Öberg, P.M. Chau, and P.H. Siegel, Reconfigurable Signal Processor for Channel Coding in Low SNR Wireless Communications, Proc. IEEE Workshop on Signal Processing Systems, Boston, Massachusetts, October 8 10, 1998. 25. B. Moision and P. H. Siegel, Distance Enhancing Constraints for Noise Predictive Maximum Likelihood Detectors, Proc. 1998 IEEE Global Telecommun. Conf. (GLOBECOM 1998) Sydney, Australia, pp. 2730 2734, November 1998. 26. T. Souvignier, A. Friedman, M. Öberg, P. H. Siegel, R. E. Swanson and J. K. Wolf, Turbo Codes for PR4: Parallel Versus Serial Concatenation, Proc. IEEE Int. Conf. Commun. Vancouver, Canada, June 7 11, 1999, S41-4. 27. A.P. des Rosiers and P.H. Siegel, Effect of Varying Source Kurtosis on the Multimoduli Algorithm, Proc. IEEE Int. Conf. Commun., Vancouver, Canada, June 7 11, 1999, Paper S33-4.

Paul H. Siegel 24 28. R.M. Roth, P.H. Siegel, and J.K. Wolf, Efficient Coding for a Two-Dimensional Runlength-Limited Constraint, Proc. SPIE Int. Symp. on Optical Sciences, Engineering, and Instrumentation, Conf. on Advanced Optical Data Storage: Materials, Systems, and Interfaces to Computers, Denver, Colorado, July 18 23, 1999, vol. 3802, Paper 05, pp. 8 17. 29. H.D. Pfister and P.H. Siegel, The Serial Concatenation of Rate-1 Codes Through Uniform Random Interleavers, Proc. 37th Annual Allerton Conf. on Commun., Control, and Computing, Monticello, Illinois, September 22 24, 1999, pp. 260 269. 30. K. Tang, P.H. Siegel, and L.B. Milstein, On the Performance of Turbo Coding for the Land Mobile Channel with Delay Constraints, Conf. Record 33rd Asilomar Conf. on Signals, Systems, and Computers, vol. 2, Pacific Grove, CA, October 24 27, 1999, pp. 1659 1664. 31. B.E. Moision, A. Orlitsky, and P.H. Siegel, Bound on the Rates of Codes that Forbid Specified Difference Sequences, Proc. IEEE Global Telecommun. Conf. GLOBECOM 1999, Rio de Janeiro, Brazil, December 5 9, 1999. 32. M. Öberg and P.H. Siegel, Parity Check Codes for Partial Response Channels, Proc. IEEE Global Telecommun. Conf. (GLOBECOM 1999), Rio de Janeiro, Brazil, December 5 9, 1999. 33. K. Tang, P.H. Siegel, and L.B. Milstein, Turbo Coding for a Coherent DS-CDMA System Employing a MMSE Receiver on a Rayleigh Fading Channel, Proc. 34th Annual Conf. Information Sciences and Systems (CISS), Princeton, NJ, March 15 17, 2000. 34. H.M. Tullberg and P.H. Siegel, Bit-Interleaved Coded Modulation for Delay- Constrained Mobile Communication Channels, Proc. IEEE Annual Vehicular Technology Conf. VTC2000-Spring, Tokyo, Japan, May 15 18, 2000, pp. 2212-2216. 35. B.E. Moision, A. Orlitsky, and P.H. Siegel, On Codes Avoiding Specified Differences, Proc. IEEE Int. Symp. Inform. Theory, Sorrento, Italy, June 25 30, 2000, p. 145. 36. R.M. Roth, P.H. Siegel, J.K. Wolf, Efficient Coding for a Two-Dimensional Runlength- Limited Constraint, Proc. IEEE Int. Symp. Inform. Theory, Sorrento, Italy, June 25 30, 2000, p. 307. 37. X.-W. Wu and P.H. Siegel, Fast Computation of Roots of Polynomials over Function Fields and Fast List Decoding of Algebraic Geometric Codes, Proc. IEEE Int. Symp. Inform. Theory, Sorrento, Italy, June 25- -30, 2000, p. 478. 38. A. Ranheim, A.P. des Rosiers, P.H. Siegel, and D.B. Rao, An Iterative Receiver Algorithm for Space-Time Encoded Signals, Conf. Record 34th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, October 2000.

Paul H. Siegel 25 39. A.P. des Rosiers and P.H. Siegel, Generalized Performance Bounds for Space Time Coded Modulation on Fading Channels, Proc. Int. Symp. Inform. Theory and its Appl. (ISITA), Honolulu, Hawaii, November 5 8, 2000. 40. H.D. Pfister and P.H. Siegel, Coding Theorems for Generalized Repeat Accumulate Codes, Proc. Int. Symp. Inform. Theory and its Appl. (ISITA), Honolulu, Hawaii, November 5 8, 2000. 41. J. Hou, P.H. Siegel, and L.B. Milstein, Performance Analysis and Code Optimization of Low Density Parity-Check Codes on Rayleigh Fading Channels, Proc. 38th Allerton Conference on Communications, Control, and Computing, Monticello, Illinois, October 4-6, 2000. 42. K.Tang, L.B. Milstein, and P.H. Siegel, A Comparison of Long versus Short Spreading Sequences in Coded Asynchronous DS-CDMA Systems, Proc. 35th Annual Conference on Information Sciences and Systems (CISS), Baltimore, Maryland, March 21-23, 2001. 43. M. Öberg and P.H. Siegel, Performance Bound for Parity-Check Coded Partial- Response Channels, Proc. IEEE Int. Conf. Commun., Helsinki, Finland, June 11 14, 2001, vol. 9, pp. 27012705. 44. B.E. Moision and P.H. Siegel, Periodic Finite-Type Shift Spaces, Proc. 1998 IEEE Int. Symp. Inform. Theory, Washington, D.C., June 25 29, 2001, p. 65 45. J. Hou, P.H. Siegel, L.B. Milstein, and H.D. Pfister, Design of Low-Density Parity- Check Codes for Bandwidth Efficient Modulation, Proc. IEEE Inform. Theory Workshop, Cairns, Australia, September 2 7, 2001, p. 24. 46. H.M. Tullberg and P.H. Siegel, Serial Concatenated Trellis Coded Modulation with Inner Rate-1 Accumulate Code, Proc. IEEE Annual Vehicular Technology Conf. (VTC2001-Fall,) Atlantic City, New Jersey, October 8 10, 2001. 47. J. Hou, P.H. Siegel, L.B. Milstein, and H.D. Pfister, Multilevel Coding with Low- Density Parity-Check Component Codes, Proc. IEEE Global Telecommunications Conference (Globecom 2001), November 25 29, 2001. 48. H.D. Pfister, J. B. Soriaga, and P.H. Siegel, On the Achievable Information Rates of Finite State ISI Channels, Proc. IEEE Global Telecommunications Conference (Globecom 2001), November 25 29, 2001. 49. H.M. Tullberg and P.H. Siegel, Serial Concatenated Trellis Coded Modulation with Inner Rate-1 Accumulate Code, Proceedings of IEEE Global Telecommunications Conference (Globecom 2001), November 25 29, 2001. 50. S. Halevy, J. Chen, R.M. Roth, P.H. Siegel, and J.K. Wolf, Improved Bit-Stuffing Bounds on Two-Dimensional Constraints, Proc. IEEE Int. Symp. Inform. Theory, June 30 July 5, 2002, Lausanne, Switzerland.

Paul H. Siegel 26 51. M. K. Cheng, J. Campello, and P. H. Siegel, Soft-Decision Reed-Solomon Decoding on Partial Response Channels, Proc. IEEE Global Telecommun. Conf. (GLOBECOM 2002), Taipei, Taiwan, November 2002, pp. 1026 1030. 52. J. Chen and P. H. Siegel, On the Symmetric Information Rate of Two-Dimensional Finite State ISI Channels, Proc. IEEE Inform. Theory Workshop, Paris, France, April 2003, pp. 320 323. 53. A. P. des Rosiers and P. H. Siegel, Space-Time Code Performance Bounds on Quasistatic Fading Channels, IEEE International Conference on Communications (ICC 2003), Anchorage, AK, May 11 15, 2003. 54. J. Chen and P. H. Siegel, Information Rates of Two-Dimensional Finite State ISI Channels, Proc. 2003 IEEE Int. Symp. Inform. Theory, Yokohama, Japan, June 29 July 4, 2003. 55. N. Kashyap and P. H. Siegel, Capacity Equalities in 1-Dimensional (d,k)- Constrained Systems, Proc. 2003 IEEE Int. Symp. Inform. Theory, Yokohama, Japan, June 29 July 4, 2003. 56. B. M. Kurkoski, P. H. Siegel, and J. K. Wolf, Precoders for Message-Passing Detection of Partial-Response Channels, Proc. IEEE Int. Symp. Inform. Theory, Yokohama, Japan, June 29 July 4, 2003. 57. H.D. Pfister and P.H. Siegel, Joint Iterative Decoding of LDPC Codes and Channels with Memory, Proc. 3rd Intl. Symp. Turbo Codes and Related Topics, Brest, France, September 1 5, 2003. 58. J.B. Soriaga and P.H. Siegel, On Distribution Shaping Codes for Partial Response Channels, Proc. 41st Allerton Conference on Communications, Control, and Computing, Monticello, Illinois, October 1 3, 2003. 59. M.K. Cheng and P.H. Siegel, Iterative Soft-Decision Reed-Solomon Decoding on Partial Response Channels, Proceedings IEEE Global Telecommun. Conf., San Francisco, California, December 1 5, 2003. 60. B.M. Kurkoski, P.H. Siegel, and J.K. Wolf, Exact Probability of Erasure and a Decoding Algorithm for Some Convolutional Codes on the Binary Erasure Channel, Proceedings IEEE Global Telecommun. Conf., San Francisco, California, December 1 5, 2003. 61. M.K. Cheng and P.H. Siegel, List-Decoding of Parity-Sharing Reed-Solomon Codes in Magnetic Recording Systems, IEEE International Conference on Communications (ICC 2004), Paris, France, June 20 24, 2004. 62. N. Kashyap, P.H. Siegel and A. Vardy, A Ramsey Theory Approach to Ghostbusting, Proc. 2004 IEEE Int. Symp. Inform. Theory, Chicago, Illinois, June 27 July 2, 2004.

Paul H. Siegel 27 63. N. Kashyap and P.H. Siegel, Sliding-Block Decodable Encoders Between (d, k)- Constrained Systems of Equal Capacity, Proc. 2004 IEEE Int. Symp. Inform. Theory, Chicago, Illinois, June 27 July 2, 2004. 64. S. Aviran, P.H. Siegel and J.K. Wolf, An Improvement to the Bit Stuffing Algorithm, Proc. 2004 IEEE Int. Symp. Inform. Theory, Chicago, Illinois, June 27 July 2, 2004. 65. B.M. Kurkoski, P.H. Siegel, and J.K. Wolf, Analysis of Convolutional Codes on the Erasure Channel, Proc. 2004 IEEE Int. Symp. Inform. Theory, Chicago, Illinois, June 27 July 2, 2004. 66. J.B. Soriaga and P.H. Siegel, On Near-Capacity Coding Systems for Partial-Response Channels, Proc. 2004 IEEE Int. Symp. Inform. Theory, Chicago, Illinois, June 27 July 2, 2004. 67. J. Han and P.H. Siegel, Reducing Acyclic Network Coding Problems to Single- Transmitter-Single-Demand Form, Proc. 42st Allerton Conference on Communications, Control, and Computing, Monticello, Illinois, September 29 October 1, 2004. 68. P. Ellingsen, O. Ytrehus, and P.H. Siegel, Enhanced Decoding by Error Detection on a Channel with Correlated 2-Dimensional Noise, Proc. IEEE Inform. Theory Workshop, San Antonio, Texas, October 24 29, 2004. 69. J. Chen and P.H. Siegel, Lower Bounds on the Capacity of Asymmetric Two- Dimensional (d, )-Constraints, Proc. 2005 IEEE Int. Symp. Inform. Theory, Adelaide, Australia, September 4 9, 2005. 70. S. Aviran, P.H. Siegel, and J.K. Wolf, Two-Dimensional Bit-Stuffing Schemes with Multiple Transformers, Proc. 2005 IEEE Int. Symp. Inform. Theory, Adelaide, Australia, September 4 9, 2005. 71. J.B. Soriaga, P.H. Siegel, M.N. Marrow, and J.K. Wolf, On Achievable Rates of Multistage Decoding on Two-Dimensional ISI Channels, Proc. 2005 IEEE Int. Symp. Inform. Theory, Adelaide, Australia, September 4 9, 2005. 72. M. Schwartz, P.H. Siegel, and A. Vardy, On the Asymptotic Performance of Iterative Decoders for Product Codes, Proc. 2005 IEEE Int. Symp. Inform. Theory, Adelaide, Australia, September 4 9, 2005. 73. P. Chaichanavong and P.H. Siegel, Relaxation Bounds on the Minimum Pseudo- Weight of Linear Block Codes, Proc. 2005 IEEE Int. Symp. Inform. Theory, Adelaide, Australia, September 4 9, 2005. 74. M.H. Taghavi N., G.C. Papen, and P.H. Siegel, Multiuser Capacity Analysis of WDM in Nonlinear Fiber Optics, Proc. 43rd Allerton Conference on Communications, Control, and Computing, Monticello, Illinois, September 28 30, 2005.

Paul H. Siegel 28 75. P.H. Siegel, Information-Theoretic Limits of Two-Dimensional Optical Recording Channels, Optical Data Storage 2006, (Proceedings of SPIE, Vol. 6282, Eds. Ryuichi Katayama and Tuviah E. Schlesinger), Montreal, Quebec, Canada, April 2006. (invited) 76. I. Demirkan, P.H. Siegel, and J.K. Wolf, Error Event Characterization on 2-D ISI Channels, Proc. 2006 IEEE Int. Symp. Inform. Theory, Seattle, Washington, July 9 July 14, 2006. 77. M.H. Taghavi and P.H. Siegel, Adaptive Linear Programming Decoding, Proc. 2006 IEEE Int. Symp. Inform. Theory, Seattle, Washington, July 9 July 14, 2006. 78. S. Aviran, P.H. Siegel, and J.K. Wolf, Optimal Parsing Trees for Run-Length Coding of Biased Data, Proc. 2006 IEEE Int. Symp. Inform. Theory, Seattle, Washington, July 9 July 14, 2006. 79. J. Han and P.H. Siegel, On the Stopping Redundancy of MDS Codes, Proc. 2006 IEEE Int. Symp. Inform. Theory, Seattle, Washington, July 9 July 14, 2006. 80. J. Han, R.M. Roth, and P.H. Siegel, Bounds on Single-Exclusion Numbers and Stopping Redundancy of MDS Codes, Proc. 2007 IEEE Int. Symp. Inform. Theory, Nice, France, June 24 29, 2007. 81. M.H. Taghavi and P.H. Siegel, Equalization on Graphs: Linear Programming and Message Passing, Proc. 2007 IEEE Int. Symp. Inform. Theory, Nice, France, June 24 29, 2007. 82. A.H. Djahanshahi, P.H. Siegel, and L.B. Milstein, Decoding on Graphs: LDPC-coded MISO Systems and Belief Propagation, IEEE Wireless Communications and Networking Conference (WCNC 2008), Las Vegas, Nevada, March/April, 2008. 83. Z. Wu, P.H. Siegel, J.K. Wolf, and H.N. Bertram, Mean-Adjusted Pattern-Dependent Noise Prediction for Perpendicular Recording Channels with Nonlinear Transition Shift, IEEE International Magnetics Conference (Intermag 2008), Madrid, Spain, May 4 8, 2008. 84. Z. Wu, P.H. Siegel, J.K. Wolf, and H.N. Bertram, Nonlinear Transition Shift and Write Precompensation in Perpendicular Magnetic Recording, IEEE International Conference on Communications (ICC 2008), Beijing, China, May 19 23, 2008. 85. J. Han and P.H. Siegel, On ML Redundancy of Codes, Proc. 2008 IEEE Int. Symp. Inform. Theory, Toronto, Canada, July 6 11, 2008. 86. O. Shental, P.H. Siegel, J.K. Wolf, D. Bickson, and D. Dolev, Gaussian Belief Propagation Solver for Systems of Linear Equations, Proc. 2008 IEEE Int. Symp. Inform. Theory, Toronto, Canada, July 6 11, 2008.

Paul H. Siegel 29 87. D. Bickson, D. Dolev, O. Shental, P.H. Siegel and J.K. Wolf, Gaussian Belief Propagation Based Multiuser Detection, Proc. 2008 IEEE Int. Symp. Inform. Theory, Toronto, Canada, July 6 11, 2008. 88. E. Yaakobi, A. Vardy, P.H. Siegel, and J.K. Wolf, Multidimensional Flash Codes, Proc. 46th Allerton Conference on Communications, Control, and Computing, Monticello, Illinois, September 24 26, 2008. 89. M.H. Taghavi, A. Shokrollahi, P.H. Siegel, Efficient Implementation of Linear Programming Decoding, Proc. 46th Allerton Conference on Communications, Control, and Computing, Monticello, Illinois, September 24 26, 2008. 90. A.H. Djahanshahi, P.H. Siegel, and L.B. Milstein, On Unequal Error Protection of Finite-Length LDPC Codes over BECs: A Scaling Approach, Proc. 2009 IEEE Int. Symp. Inform. Theory, Seoul, Korea, June 28 July 3, 2009. 91. A. Jiang, R. Mateescu, E. Yaakobi, J. Bruck, P.H. Siegel, A. Vardy, and J.K. Wolf, Storage Coding for Wear Leveling in Flash Memories, Proc. 2009 IEEE Int. Symp. Inform. Theory, Seoul, Korea, June 28 July 3, 2009. 92. H. Mahdavifar, P.H. Siegel, A. Vardy, J.K. Wolf, and E. Yaakobi, A Nearly Optimal Construction of Flash Codes, Proc. 2009 IEEE Int. Symp. Inform. Theory, Seoul, Korea, June 28 July 3, 2009. 93. A.R. Iyengar, P.H. Siegel, and J.K. Wolf, LDPC Codes for the Cascaded BSC- BAWGN Channel, Proc. 47th Allerton Conference on Communications, Control, and Computing, Monticello, Illinois, September 30 October 2 2009. 94. L. Grupp, A. Caulfield, J. Coburn, S. Swanson, E. Yaakobi, P.H. Siegel, and J.K. Wolf, Characterizing Flash Memory : Anomalies, Observations, and Applications, MICRO 09, December 2009. 95. M. Papaleo, A.R. Iyengar, P.H. Siegel, J.K. Wolf, and G.E. Corazza, Windowed Erasure Decoding of LDPC Convolutional Codes, Proc. IEEE Inform. Theory Workshop, Cairo, Egypt, January 6 8, 2010. 96. A.R. Iyengar, M. Papaleo, G. Liva, P. H. Siegel, J.K. Wolf, and G.E. Corazza, Protograph-Based LDPC Convolutional Codes for Correlated Erasure Channels, IEEE International Conference on Communications (ICC 2010), Cape Town, South Africa, May 23 27, 2010. 97. A. Iyengar, P.H. Siegel, and J.K. Wolf, Data-Dependent Write Channel Model for Magnetic Recording, Proc. 2009 IEEE Int. Symp. Inform. Theory, Austin, Texas, June 13 18, 2010. 98. B.K. Butler and P.H. Siegel, On Distance Properties of Quasi-Cyclic Protograph- Based LDPC Codes, Proc. 2009 IEEE Int. Symp. Inform. Theory, Austin, Texas, June 13 18, 2010.

Paul H. Siegel 30 99. E. Yaakobi, P.H. Siegel, A. Vardy, and J.K. Wolf, Multiple Error- Correcting WOM- Codes, Proc. 2009 IEEE Int. Symp. Inform. Theory, Austin, Texas, June 13 18, 2010. 100. A.R. Iyengar, P.H. Siegel, and J.K. Wolf, Write Channel Model for Bit-Patterned Media Recording, 21st Magnetic Recording Conference (TMRC), La Jolla, CA, USA, August 16 18, 2010. 101. E. Yaakobi, A. Jiang, P.H. Siegel, A. Vardy, and J.K. Wolf, On the Parallel Programming of Flash Memory Cells, Proc. IEEE Inform. Theory Workshop, Dublin, Ireland, August 30 September 3, 2010. 102. E. Yaakobi, S. Kayser, P.H. Siegel, A. Vardy, and J.K. Wolf, Efficient Two-Write WOM-Codes, Proc. IEEE Inform. Theory Workshop, Dublin, Ireland, August 30 September 3, 2010. 103. G.E. Corazza, A.R. Iyengar, M. Papaleo, P.H. Siegel, A. Vanelli-Coralli, and J.K. Wolf, Latency Constrained Protograph-Based LDPC Convolutional Codes, 6th International Symposium on Turbo Codes and Iterative Information Processing, Brest France, September 6 10, 2010. 104. S. Kayser, E. Yaakobi, P.H. Siegel, A. Vardy, and J.K. Wolf, Multiple-Write WOM- Codes, Proc. 48th Allerton Conference on Communications, Control, and Computing, Monticello, Illinois, September 29 October 1, 2010. 105. E. Yaakobi, J. Ma, L. Grupp, P.H. Siegel, S. Swanson, and J.K. Wolf, Error Characterization and Coding Schemes for Flash Memories, Proc. IEEE Global Telecommun. Conf. (GLOBECOM 2010), Miami, Florida, December 2010. 106. A.R. Iyengar, P.H. Siegel, and J.K. Wolf, Modeling and Information Rates for Synchronization Error Channels, Proc. 2011 IEEE Int. Symp. Inform. Theory, St. Petersburg, Russia, July 31 August 5, 2011. 107. A.R. Iyengar, P.H. Siegel, R.L. Urbanke, and J.K. Wolf, Windowed Decoding of Spatially Coupled Codes, Proc. 2011 IEEE Int. Symp. Inform. Theory, St. Petersburg, Russia, July 31 August 5, 2011. 108. E. Yaakobi, P.H. Siegel, A. Vardy, and J.K. Wolf, On Codes that Correct Asymmetric Errors with Graded Magnitude Distribution, Proc. 2011 IEEE Int. Symp. Inform. Theory, St. Petersburg, Russia, July 31 August 5, 2011. 109. X. Zhang, and P.H. Siegel, Adaptive Cut Generation for Improved Linear Programming Decoding of Binary Linear Codes, Proc. 2011 IEEE Int. Symp. Inform. Theory, St. Petersburg, Russia, July 31 August 5, 2011. 110. R. Gabrys, E. Yaakobi, L. Dolecek, P.H. Siegel, A. Vardy, and J.K. Wolf, Non-binary WOM-Codes for Multilevel Flash Memories, Proc. IEEE Inform. Theory Workshop, Paraty, Brazil, October 16 20, 2011.

Paul H. Siegel 31 111. A. Bhatia, A. R. Iyengar and P. H. Siegel, Enhancing Binary Images of Non-Binary LDPC Codes, Proc. IEEE Global Telecommun. Conf. (GLOBECOM 2011), Houston, Texas, December 5 9, 2011. 112. M. Qin, E. Yaakobi, and P.H. Siegel, Time-Space Constrained Codes for Phase- Change Memories, Proc. IEEE Global Telecommun. Conf. (GLOBECOM 2011), Houston, Texas, December 5 9, 2011. 113. X. Zhang and P.H. Siegel, Efficient Algorithms to Find All Small Error-Prone Substructures in LDPC Codes, Proc. IEEE Global Telecommun. Conf. (GLOBECOM 2011), Houston, Texas, December 5 9, 2011. 114. E. Yaakobi, L. Grupp, P.H. Siegel, S. Swanson, J.K. Wolf, Characterization and Error-Correcting Codes for TLC Flash Memories, Proc. IEEE Int. Conf. Computing, Networking, and Communications (ICNC), Maui, Hawaii, January 30 February 2, 2012. 115. E. Yaakobi, J. Bruck, P.H. Siegel, Decoding of Cyclic Codes over Symbol-Pair Read Channels, Proc. 2012 IEEE Int. Symp. Inform. Theory, Cambridge, Massachusetts, July 1 6, 2012. 116. L. Wang, M. Qin, E. Yaakobi, Y.-H. Kim, P.H. Siegel, WOM with Retained Messages, Proc. 2012 IEEE Int. Symp. Inform. Theory, Cambridge, Massachusetts, July 1 6, 2012. 117. M. Qin, E. Yaakobi, P.H. Siegel, Optimized Cell Programming for Flash Memories with Quantizers, Proc. 2012 IEEE Int. Symp. Inform. Theory, Cambridge, Massachusetts, July 1 6, 2012. 118. X. Zhang and P.H. Siegel, Quantized Min-Sum Decoders with Low Error Floor for LDPC Codes, Proc. 2012 IEEE Int. Symp. Inform. Theory, Cambridge, Massachusetts, July 1 6, 2012. 119. X. Zhang and P.H. Siegel, Will the Real Error Floor Please Stand Up?, Proc. Signal Processing and Communications (SPCOM), Bangalore, India, July 22 25, 2012. 120. A. Bhatia, A.R. Iyengar, P.H. Siegel, Multilevel 2-Cell t-write Codes, IEEE Information Theory Workshop (ITW), Lausanne, Switzerland, September 3 7, 2012. 121. S. Sharifi Tehrani, P.H. Siegel, S. Mannor, and W.J. Gross, Joint Stochastic Decoding of LDPC Codes and Partial-Response Channels, Proc. IEEE Workshop on Signal Processing Systems (SiPS), October 17 19, 2012. 122. B.K. Butler and P.H. Siegel, Numerical Issues Affecting LDPC Error Floors, Proc. IEEE Global Telecommun. Conf. (GLOBECOM 2012), Anaheim, California, December 3 7, 2012.

Paul H. Siegel 32 123. B. Peleato, R. Agarwal, J. Cioffi, M. Qin, and P.H. Siegel, Towards Minimizing Read Time for NAND Flash, Proc. IEEE Global Telecommun. Conf. (GLOBECOM 2012), Anaheim, California, December 3 7, 2012. 124. M. Qin, A.(A.) Jiang, and P.H. Siegel, Parallel Programming of Rank Modulation, Proc. 2013 IEEE Int. Symp. Inform. Theory, Istanbul, Turkey, July 7 12, 2013. 125. X. Zhang and P.H. Siegel, Efficient Iterative LP Decoding of LDPC Codes with Alternating Direction Method of Multipliers, Proc. 2013 IEEE Int. Symp. Inform. Theory, Istanbul, Turkey, July 7 12, 2013. 126. S. Kayser and P. H. Siegel, Constructions for Constant-weight ICI-free Codes, Proc. 2014 IEEE Int. Symp. Inform. Theory, Honolulu, Hawaii, June 29 July 5, 2014. 127. V. Taranalli and P. H. Siegel, Adaptive Linear Programming Decoding of Polar Codes, Proc. 2014 IEEE Int. Symp. Inform. Theory, Honolulu, Hawaii, June 29 July 5, 2014. 128. J. Guo, M. Qin, A. Guillen I Fabregas, and P. H. Siegel, Enhanced Belief Propagation Decoding of Polar Codes Through Concatenation, Proc. 2014 IEEE Int. Symp. Inform. Theory, Honolulu, Hawaii, June 29 July 5, 2014. 129. A. Bhatia, V. Taranalli, P. H. Siegel, S. Dahandeh, A. R. Krishnan, P. Lee, D. Qin, M. Sharma, and T.Yeo, Polar Codes for Magnetic Recording Channels, Proc. IEEE Information Theory Workshop, Jerusalem, Israel, April 26 May 1, 2015. 130. P. Huang, E. Yaakobi, H. Uchikawa, and P. H. Siegel, Cyclic Linear Binary Locally Repairable Codes, Proc. IEEE Information Theory Workshop, Jerusalem, Israel, April 26 May 1, 2015. 131. B. Fan, H. K. Thapar, and P. H. Siegel, Multihead Multitrack Detection with Reduced-State Sequence Estimation in Shingled Magnetic Recording, Proc. IEEE Int. Magn. Conf. (INTERMAG), Beijing, China, May 11 15, 2015. 132. V. Taranalli, H. Uchikawa, and P. H. Siegel, Error Analysis and Inter-Cell Interference Mitigation in Multi-Level Cell Flash Memories, Proc. IEEE Int. Conf. Communications (ICC), London, June 8 12, 2015, pp. pp. 271 276. 133. B. Fan, H. K. Thapar, and P. H. Siegel, Multihead Multitrack Detection in Shingled Magnetic Recording with ITI Estimation, Proc. IEEE International Conference on Commun. (ICC), London, June 8 12, 2015, pp. 425 430. 134. S. Buzaglo, P. H. Siegel, and E. Yaakobi, Coding Schemes for Inter-Cell Interference in Flash Memory, Proc. IEEE Int. Symp. Inform. Theory (ISIT), Hong Kong, June 13 20, 2015, pp. 1736 1740.

Paul H. Siegel 33 135. P. Huang, E. Yaakobi, H. Uchikawa, and P. H. Siegel, Linear Locally Repairable Codes with Availability, Proc. IEEE Int. Symp. Inform. Theory (ISIT), Hong Kong, June 13 20, 2015, pp. 1871 1875. 136. S. Buzaglo, E. Yaakobi, Y. Cassuto, and P. H. Siegel, Consecutive Switch Codes, Proc. IEEE Int. Symp. Inform. Theory (ISIT), Barcelona, July 10 15, 2016, pp. 660 664. 137. P. Huang, E. Yaakobi, and P. H. Siegel, Performance of Flash Memories with Different Binary Labelings: A Multi-user Perspective, Proc. IEEE Int. Symp. Inform. Theory (ISIT), Barcelona, July 10 15, 2016, pp. 955 959. 138. Y. Liu and P. H. Siegel, Shaping Codes for Structured Data, Proc. IEEE Global Commun. Conf. (GLOBECOM), Washington, D.C., December 4-8, 2016, pp. 1 6. 139. S. Buzaglo, A. Fazeli, P. H. Siegel, V. Taranalli, and A. Vardy, On Efficient Decoding of Polar Codes with Large Kernels, Proc. IEEE Wireless Commun. Network. Conf. (WCNC), San Francisco, CA, March 19 22, 2017. 140. R. Gabrys, E. Yaakobi, M. Blaum, and P. H. Siegel, Constructions of Partial MDS Codes over Small Fields, Proc. IEEE Int. Symp. Inform. Theory, Aachen, Germany, June 25-30, 2017, pp. 1 5. 141. Y. Liu, P. Huang, and P. H. Siegel, Performance of Optimal Data Shaping Codes, Proc. IEEE Int. Symp. Inform. Theory, Aachen, Germany, June 25 30, 2017, pp. 1003 1007. 142. S. Buzaglo, A. Fazeli, P. H. Siegel, V. Taranalli, and A. Vardy, Permuted Successive Cancellation Decoding for Polar Codes, Proc. IEEE Int. Symp. Inform. Theory, Aachen, Germany, June 25 30, 2017, pp. 2623 2627. Other Conference Abstracts and Digests 1. P. Siegel, Spectral Null Codes and Number Theory, Proc. 1990 IEEE Int. Work. on Inform. Th., Veldhoven, The Netherlands, June 1990, p. 34.(invited) 2. C. Shung, P. Siegel, H. Thapar, and R. Karabed, A 30 MHz Trellis Codec Chip for Partial Response Channels, Dig. 1991 IEEE Int. Solid-state Circuits Conf., San Francisco, February 1991, pp. 132 133. 3. P.H. Siegel, Constructions of Reduced Truncation Depth Trellis Codes for Magnetic Recording, Proc. Coding Theory Gathering, vol. 2, Bergen, Norway, June 1994, pp. 239 254.(invited) 4. R. Roth, P. Siegel, and A. Vardy, High-Order Spectral-Null Codes: Constructions and Bounds, Proc. 1994 IEEE Int. Symp. Inform. Th., Trondheim, Norway, June 1994, p. 205.

Paul H. Siegel 34 5. P.H. Siegel, Coded Modulation for Binary Partial Response Channels: State-of-the- Art, Abstracts 1996 IEEE Inform. Th. Workshop, Haifa, Israel, June 1996. (invited) 6. P. Siegel, Coding for Multi-Dimensional Storage, Abstracts 1997 IEEE Inform. Theory. Workshop, Longyearbyen, Svalbard, July 1997, p. 25. (invited) 7. M. Öberg, H.D. Pfister, and P.H. Siegel, Parity-Accumulate Codes for Magnetic Recording, Digest of Technical Papers, IEEE Int. Magn. Conf., Toronto, Canada, April 9 13, 2000, p. 517. (invited) 8. B.M. Kurkoski, P.H. Siegel, and J.K. Wolf, Vector Quantization of the BCJR Algorithm s State Metrics for Partial Response Channels, 4th Asia-Europe Workshop on Information Theory, Viareggio, Italy, Ocotber 6 8, 2004. 9. S. Aviran, P. H. Siegel, and J. K. Wolf, Noise-Predictive Turbo Equalization for Partial-Response Channels, Digest of Technical Papers, IEEE Int. Magn. Conf., Nagoya, Japan, April 4 8, 2005, pp. 981-982. 10. B. M. Kurkoski, P. H. Siegel, J. K. Wolf, Soft-Output Detector for Partial-Response Channels using Vector Quantization, Digest of Technical Papers, IEEE Int. Magn. Conf., Nagoya, Japan, April 4 8, 2005, pp. 1601-1602. 11. P. H. Siegel, Information-Theoretic Limits of Two-Dimensional Optical Recording Channels, Abstracts of Optical Data Storage Topical Meeting 2006, Montreal, Quebec, Canada, April 23-26, 2006, pp. 165 167. (invited) 12. P. Chaichanavong, H. N. Bertram and P. H. Siegel, Design Parameter Optimization for Perpendicular Magnetic Recording Systems, Digests of Technical Papers, IEEE Int. Magn. Conf., San Diego, CA, May 8 12, 2006, p. 294. (invited) 13. Z. Wu, P.H. Siegel, H.N. Bertram, and J.K. Wolf, Design Curves and Information Theoretic Limits for Perpendicular Recording Systems, Digests of Technical Papers, IEEE Magn. Rec. Conf., Pittsburgh, PA, Aug. 7 9, 2006. (invited) 14. M.H. Taghavi and P.H. Siegel, Graph-Based Decoding in the Presence of ISI, Abstracts of Conf. on Algorithms, Inference, and Statistical Physics, Santa Fe, NM, May 1 4, 2007. 15. S. Karakulak, P.H. Siegel, J.K. Wolf, and H.N. Bertram, A New Read Head Model for Patterned Media Storage, Digests of Technical Papers, IEEE Magn. Rec. Conf., Minneapolis, MN, May 21 23, 2007. (invited) 16. O. Shental, D. Bickson, P.H. Siegel, J.K. Wolf, D. Dolev, A Message-Passing Solver for Linear Systems, Information Theory and Applications Workshop, La Jolla, CA, Jan. 27 Feb. 1, 2008.

Paul H. Siegel 35 17. L. Grupp, A. Caulfield, J. Coburn, E. Yaakobi, S. Swanson, and P.H. Siegel, Characterizing Flash Memory: Beyond the Datasheet, Flash Memory Summit, Santa Clara, CA, August 2009. 18. E. Yaakobi, J. Ma, A. Caulfield, L. Grupp, S. Swanson, P.H. Siegel, and J.K. Wolf, Error Correction Coding for Flash Memories, Flash Memory Summit, Santa Clara, CA, August 2009. 19. L.M. Grupp, H.-W. Tseng, A.M. Caulfield, J. Coburn, E. Yaakobi, P..H. Siegel, J.K. Wolf and S. Swanson, Finding Flash Features: Bigger, Better, Faster... Stronger ECC, Flash Memory Summit, Santa Clara, CA, August 2010. 20. E. Yaakobi, L. Grupp, S. Swanson, P.H. Siegel, and J.K. Wolf, Efficient Coding Schemes for Flash Memories, Flash Memory Summit 2010 Proceedings, Santa Clara, CA, August 2010. 21. E. Yaakobi, L. Grupp, S. Swanson, P.H. Siegel, and J.K. Wolf, Error-Correcting Codes for TLC Flash, Flash Memory Summit 2011 Proceedings, Santa Clara, CA, August 9-11, 2011. 22. M. Qin, E. Yaakobi, and P.H. Siegel, Time-Space Constrained Codes for Phase- Change Memories, Flash Memory Summit 2011 Proceedings, Santa Clara, CA, August 9-11, 2011. 23. J. Weber, K.A.S. Immink, P.H. Siegel, and T. Swart, Polarity-Balanced Codes, Information Theory and Applications Workshop (ITA), La Jolla, CA, February 10 15, 2013. 24. Y. Liu and P.H. Siegel, Shaping Codes for Structured Data, 7th Annual Non-Volatile Memories Workshop (NVMW), La Jolla, CA, March 6 8, 2016. 25. V. Taranalli, H. Uchikawa, and P. H. Siegel, Channel Models for Multi-Level Cell Flash Memories Based on Empirical Error Analysis, 7th Annual Non-Volatile Memories Workshop (NVMW), La Jolla, CA, March 6 8, 2016. 26. Y. Liu, P. Huang, and P. H. Siegel, Performance of Shaping Codes for Flash Memory, 8th Annual Non-Volatile Memories Workshop (NVMW), La Jolla, CA, March 12 14, 2017. 27. UCSD O. Torii and P. H. Siegel Novel ICI-mitigation Codes for MLC NAND Flash Memory, 8th Annual Non-Volatile Memories Workshop (NVMW), La Jolla, CA, March 12 14, 2017.

Paul H. Siegel 36 Selected Invited Presentations 1. IEEE International Magnetics Conference (Intermag 85), Invited Session on Magnetic Recording, May 1985, St. Paul, Minnesota. 2. Workshop on Coding, Modulation and Signal Processing, May 1985 Center for Magnetic Recording Research, University of California at San Diego, La Jolla, California. 3. Optical Society of America Annual Meeting, October 1985, Washington, DC. 4. Short Course on Signal Processing for Digital Magnetic Recording, March 1986, Institute for Information Storage Technology, Santa Clara, California. 5. International Symposium on Information Theory, October 1986, Ann Arbor, Michigan. 6. Workshop on Coding, Modulation and Signal Processing, January 1987, Center for Magnetic Recording Research, University of California at San Diego, La Jolla, California. 7. Workshop on Topological Markov Shifts and Coding, Heidelberg University, July 1987. 8. Conference on Communications, Control, and Signal Processing, April 1988, CalTech, Pasadena, California. 9. IEEE International Conference on Communications (ICC 89), June 1989, Boston, Massachusetts. 10. IEEE International Workshop on Information Theory, June 1990, Veldhoven, The Netherlands. 11. IEEE International Conference on Computer Design. September 1990, Cambridge, Massachusetts. 12. 1990 IEEE Global Telecommunications Conference (Globecom 90), December 1990, San Diego, California. 13. Short Course on The Impact of DSP on Future Generation High Density Disk Drives, Institute for Information Storage Technology, December 1991, Santa Clara, California. 14. IEEE International Magnetics Conference (Intermag 92), St. Louis, Missouri (presented by H. Thapar). 15. IEEE International Symposium on Signals, Systems, and Electronics (ISSSE 92), September 1992, Paris, France. 16. Allerton Conference on Communications, Control, and Computing, October 1992, Allerton, Illinois.

Paul H. Siegel 37 17. Asilomar Conference on Signals, Systems, and Computers, October 1992, Pacific Grove, California. 18. University of Michigan, Winter Semester Seminar Series, Department of Electrical Engineering and Computer Science, April 15, 1993. 19. IEEE Information Theory Society Workshop on Coding, System Theory, and Symbolic Dynamics, Mansfield, Massachusetts, October 1993. 20. Workshop on Coding, Department of Informatics, University of Bergen, Norway, June 1994. 21. Short Course on Sustaining a 60% CGR: The Challenges for Hard Disk Drive Heads/Media/Channels, Institute for Information Storage Technology, December 1994, Santa Clara, California. 22. American Mathematical Society Short Course on Coding Theory, San Francisco, California, January 1995. 23. IEEE Information Theory Workshop, Haifa, Israel, June 1996. 24. Workshop on The Mathematics of Information Coding, Extraction, and Distribution,Institute for Mathematics and its Applications (IMA), Minneapolis, Minnesota, November 1996. 25. IEEE Information Theory Workshop, Svalbard, Norway, July 1997. 26. Allerton Conference on Communications, Control, and Computing, Allerton, Illinois, October 1997. 27. Allerton Conference on Communications, Control, and Computing, Allerton, Illinois, October 1998. 28. Conference on Advanced Optical Memories and Interfaces to Computer Systems II, SPIE International Symposium on Optical Science, Engineering, and Instrumentation, Denver, Colorado, July 1999. 29. Workshop on Codes, Systems, and Graphical Models, Institute for Mathematics and Its Applications(IMA), Minneapolis, Minnesota, August 1999. 30. Allerton Conference on Communications, Control, and Computing, Allerton, Illinois, September 1999. 31. IBM Workshop on Magnetic Recording, IBM Zurich Research Laboratory, Zurich, Switzerland, November 1999. 32. IEEE International Magnetics Conference (Intermag 2000), Session on Iterative Detection and Timing Recovery, Toronto, Ontario, Canada, April 2000.

Paul H. Siegel 38 33. IEEE Communication Theory Workshop, Borrego Springs, California, May 2001. 34. Workshop in Honor of Prof. Robert J. McElieces 60th Birthday, California Institute of Technology, Pasadena, California, May 2002. 35. Summer Research Institute, Swiss Federal Polytechnic University of Lausanne (EPFL), July 2002. 36. Hitachi Central Research Laboratory, Tokyp, Japan, July 2003. 37. Workshop on Applications of Statistical Physics to Coding Theory, Center for Nonlinear Studies (Los Alamos National Laboratory), Santa Fe, New Mexico, January 2005. 38. Distinguished Speaker Series, Department of Electrical and Computer Engineering, University of Arizona, February 2005. 39. Viterbi Conference, University of Southern California, Los Angeles, California, March 2005. 40. Toshiba Corporation, Tokyo, Japan, April 2005. 41. Fujitsu, Limited, Tokyo, Japan, April 2005. 42. Department of Information and Communication Engineering, University of Electro- Communications, Chofu, Tokyo, Japan, April 2005. 43. Information Storage Industry Consortium, Extremely High Density Recording Research Review Joint Session, Monterey, California, July 2005. 44. Department of Mathematics, Politecnico di Torino, Turin, Italy, May 2-25, 2006. (invited course on Coding for storage systems, 24 lecture hours) 45. Information Engineering Department, University of Parma, Parma, Italy, May 19, 2006. 46. Telecommunications Group, Politecnico di Torino, Turin, Italy, May 23, 2006. 47. Tutorial on Low-Density Parity-Check Codes, Center for Wireless Communications, Research Review, May 31, 2007. 48. Keynote Address, QTech Forum, Qualcomm Incorporated, November 6, 2007. 49. Institut Gaspard-Monge, Laboratoire d informatique, Université Paris-Est, Marne-la- Vallée, June 17, 2008. 50. Distinguished Speaker Series, Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, April 5, 2011.

Paul H. Siegel 39 51. Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, April 5, 2011. 52. Computer Science Colloquium, Technion - Israel Institute of Technology, June 21, 2011. 53. Workshop on Emerging Data Storage Technologies, IEEE International Conference on Communications, Ottowa, Canada, June 11, 2012. 54. Session on Coding Theory for the Next-Generation Storage Systems, Asilomar Conference on Signals, Systems & Computers, Pacific Grove, California, November 4 7, 2012. 55. Seminaires ICI, Ecole National Superieure de l Electronique et de ses Applications (ENSEA), University of Cergy-Pontoise, France, December 5, 2012. 56. IEEE Information Theory Workshop, Haifa, Israel, April 26-May 1, 2015. 57. Workshop on Coding for Emerging Memories and Storage Technologies, Technion, Haifa, Israel, May 3, 2015. 58. IEEE Information Theory Society Padovani Lecture, 2015 North American School of Information Theory, La Jolla, CA, August 12, 2015. 59. Department of Electrical Engineering Seminar, UCLA, Los Angeles, CA, February 29, 2016. 60. 7th Annual Non-Volatile Memories Workshop (NVMW), Tutorial, La Jolla, CA, March 6, 2016. 61. 3rd International Storage Technology Conference, Western Digital Corporation, Milpitas, CA, June 14, 2017. 62. The 7th Annual Henry Taub TCE Conference, Coding for Storage and Information Systems, Technion, Haifa, Israel, June 22, 2017.

Education Alexander Vardy Biographical Sketch Ph.D. Electrical Engineering: Tel-Aviv University, 1991 B.Sc. Electrical Engineering: Technion Israel Institute of Technology, 1985 Academic and Professional Experience 2000 present University of California, San Diego, Professor 1998 2000 University of California, San Diego, Associate Professor 1997 1998 University of Illinois at Urbana Champaign, Associate Professor 1993 1997 University of Illinois at Urbana Champaign, Assistant Professor 1992 1993 IBM Research Division, Almaden Research Center, CA., Visiting Scientist 1985 1990 Israeli Air Force, Systems Division, R&D Engineer and Project Manager Long-term visiting positions: Centre Nationale de Recherche Scientifique (CNRS), France, from 8/1998 to 6/1999, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, from 5/2005 to 2/2006, the Technion, Israel, from 3/2006 to 1/2007, Nanyang Technological University, Singapore, from 7/2015 to 9/2016. Honors and Awards IEEE Communications Society & Information Theory Society Joint Paper Award, 2017 (for the paper List Decoding of Polar Codes) IEEE International Conference on RFID, Best Paper Award, 2015 (for the paper Coding for Tag Collision Recovery) Jack Keil Wolf Endowed Chair Professorship, University of California San Diego, 2012 Thomson Scientific ISI (Science Citation Index) highly-cited researcher, 2007 (among the 250 most-cited researchers worldwide in the field of computer science) J. William Fulbright Senior Scholar Fellowship, 2006 IEEE Symposium on Foundations of Computer Science (FOCS), Best Paper Award, 2005 (for the paper Correcting Errors Beyond the Guruswami-Sudan Radius in Polynomial Time) IEEE Information Theory Society Paper Award, 2004 (for the paper Algebraic Soft-Decision Decoding of Reed-Solomon Codes) Fellow, Institute of Electrical and Electronics Engineers (IEEE), 1999 Xerox Award for Faculty Research, University of Illinois, 1996 David and Lucile Packard Fellowship for Science and Engineering, 1996 Fellow, Center for Advanced Study, University of Illinois, 1996 NSF Faculty Early Career Development (CAREER) Award, 1995 List of Teachers Ranked Excellent by their Students, University of Illinois, 1994 and 1997 IBM Invention Achievement Award, 1993 Synergistic Activities: Editorial Service Executive Editorial Board, IEEE TRANSACTIONS ON INFORMATION THEORY, 2010 present Editor-in-Chief, IEEE TRANSACTIONS ON INFORMATION THEORY, 1998 2001 Editor, SIAM JOURNAL ON DISCRETE MATHEMATICS, 2003 2009 Associate Editor for Coding Theory, IEEE TRANSACTIONS ON INFORMATION THEORY, 1995 1998 Guest Editor, special issues of the IEEE TRANS. ON INFORMATION THEORY, in 1996 and 2011 Program Co-Chair, IEEE International Symposium on Information Theory, 2012 Program Chair, Allerton Conference on Communication, Control, and Computing, 1997 Co-Chair, IMA Workshop on Codes, Systems, and Graphical Models, 1999 Co-Chair, Workshop on Information Theory & Applications, in 2006, 2007, and 2010.

Synergistic Activities: Other Professional Service Nominator, MacArthur Fellowships, MACARTHUR FOUNDATION, 2003 present BOARD OF GOVERNORS, IEEE Information Theory Society, 1998 2007 and 2011 present Plenary Speaker, IEEE International Symposium on Information Theory (ISIT), 2006 Plenary Speaker, 12-th Biennial SIAM Conference on Discrete Mathematics (SIAM-DM), 2004 Plenary Speaker, ACM Symposium on Theory of Computing (STOC), 1997 Shannon Award Committee, IEEE Information Theory Society, 2000 2001 NSF Panelist for CISE, Communication and Information Foundations Graduate and Postdoctoral Students (the former with Ph.D. date) Dakshi Agrawal (Ph.D. 1999), Erik Agrell, Arman Fazeli (Ph.D. 2017, expected), Sreechakra Goparaju, Navin Kashyap, Ralf Koetter, Jun Ma (Ph.D. 2007), Hessam Mahdavifar (Ph.D. 2012), Farzad Parvaresh (Ph.D. 2007), Sankeerth Rao, Nandu Santhi (Ph.D. 2006), Eren Şaşoğlu, Moshe Schwartz, Ido Tal, Himanshu Tyagi, Vahid Tarokh, Ari Trachtenberg (Ph.D. 2000), Eitan Yaakobi (Ph.D. 2011) Other Collaborators (during the past 48 months; in alphabetical order) Michael Braun, Tuvi Etzion, Pascal Giard, Warren Gross, Camille Leroux, Simon Litsyn, Shachar Lovett, Mehul Motani, Patric Östergård, Noam Presman, Alexandre Raymond, Gabi Sarkis, Ofer Shapira, Paul Siegel, Vincent Tan, Claude Thibeault, Eldho Thomas, Alfred Wassermann, Jack Wolf Five Relevant Publications: [1] T. Etzion and A. Vardy, Error-correcting codes in projective space, IEEE Transactions on Information Theory, vol. 57, no. 2, pp. 1165 1173, February 2011. [2] A. Fazeli, A. Vardy, and E. Yaakobi, Generalized sphere packing bound, IEEE Transactions on Information Theory, vol. 61, no. 5, pp. 2313 2334, May 2015. [3] A. Fazeli, A. Vardy, and E. Yaakobi, Codes for distributed PIR with low storage overhead, Proceedings IEEE International Symp. Information Theory, pp. 2852 2856, Hong Kong, China, June 2015. [4] S. Goparaju, A. Fazeli, and A. Vardy, Minimum storage regenerating codes for all parameters, Proceedings IEEE International Symp. Information Theory, pp. 76 80, Barcelona, Spain, July 2016; also submitted for publication in the IEEE Transactions on Information Theory, August 2016. [5] A. Vardy and E. Yaakobi, Constructions of batch codes with near-optimal redundancy, Proceedings of the IEEE International Symp. Information Theory, pp. 1197 1201, Barcelona, Spain, July 2016. Five Irrelevant Publications: [1] A. Vardy, A new sphere packing in 20 dimensions, Inventiones Mathematicae, Springer-Verlag, vol. 121, pp. 119 133, July 1995. [2] A. Vardy, The intractability of computing the minimum distance of a code, IEEE Transactions on Information Theory, vol. 43, pp. 1757 1766, November 1997. [3] A. Vardy, Trellis Structure of Codes, Chapter 24, pp. 1989 2118 in HANDBOOK OF CODING THE- ORY (V.S. Pless and W.C. Huffman, Editors), Amsterdam: Elsevier, December 1998. [4] R. Koetter and A. Vardy, Algebraic soft-decision decoding of Reed-Solomon codes, IEEE Transactions on Information Theory, vol. 49, pp. 2809 2825, November 2003 (IT Society Paper Award). [5] F. Parvaresh and A. Vardy, Correcting errors beyond the Guruswami-Sudan radius in polynomial time, Proceedings of the 46-th IEEE Symposium on Foundations of Computer Science (FOCS), pp. 285 294, Pittsburgh, PA, October 2005 (Best Paper Award). 2

NUNO VASCONCELOS Professor Department of Electrical and Computer Engineering University of California, San Diego Phone: 858-534-5550 E-mail: nuno@ece.ucsd.edu http://ww.svcl.ucsd.edu/~nuno EDUCATION 1988 Licenciatura Universidade do Porto, Portugal, Electrical Engineering & Computer Science 1993 M.S., Massachusetts Institute of Technology, Media Arts & Sciences 2000 Ph.D. Massachusetts Institute of Technology, Media Arts & Sciences ACADEMIC EMPLOYMENT 1988-1991 Researcher, INESC Instituto de Engenharia de Sistemas e Computadores, Porto, Portugal 1991-2000 Research Assistant, MIT Media Laboratory, Boston, MA 1996 Summer Intern, Digital Equipment Corp., Massachusetts 2000-2002 Research Staff Member, Compaq Computer Corp, Cambridge Research Lab, MA 2002-2003 Research Staff Member, Hewlett Packard, Cambridge Research Lab, MA 2003-2008 Assistant Professor, University of California, San Diego 2008-2011 Associate Professor, University of California, San Diego 2011-Present Professor, University of California, San Diego RESEARCH INTERESTS Computer vision, machine learning, statistical signal processing, multimedia. HONORS AND AWARDS 1991-1993 Fellowship from the Fundacao Luso-Americana para Desenvolvimento 1993-1997 Fellowship from the Junta Nacional para a Investigacao Cientificae Technologica 2005-2006 Hellman Fellow 2005-2010 NSF Career Award 2017 IEEE Fellow

MEMBERSHIPS Fellow, Institute of Electrical and Electronics Engineers (IEEE) Portuguese American Post-Graduate Society EDITORIAL POSITIONS 2001 Chair, Special Session on Content-Based Image Retrieval from Image Databases, IEEE Int. Conf. Image Processing, Thessaloniki, Greece. 2005 Demos and Exhibits Chair, IEEE Conference on Computer Vision and Pattern Recognition 2005 Guest Editor, Special Issue in Image Retrieval, EURASIP Signal Processing Journal 2012 Area Chair, IEEE Conference on Computer Vision and Pattern Recognition 2013 Area Chair, Iberian Conference in Pattern Recognition and Image Analysis 2013 Area Chair, Neural Information Processing Systems 2013-present Senior Area Editor, IEEE Signal Processing Letters 2014 Area Chair, IEEE Conference on Computer Vision and Pattern Recognition 2014 Area Chair, International Conference in Machine Learning 2014 Area Chair, International Conference in Pattern Recognition 2015 Area Chair, International Conference on Computer Vision 2015 Area Chair, International Conference in Machine Learning 2015 Program Chair, ACM International Conference on Multimedia Retrieval 2015 Demo Chair, Winter Conference on Applications of Computer Vision 2015 Associate Editor, IEEE Transactions on Neural Networks and Learning Systems, Special Issue in Visual Saliency Computing and Learning 2016 Area Chair, IEEE Conference on Computer Vision and Pattern Recognition 2016 Area Chair, International Conference in Pattern Recognition 2016 Area Chair, European Conference in Computer Vision 2017 Area Chair, Internatonal Conference in Computer Vision FUNDING 1. Career: Weakly Supervised Recognition, NSF, 4/15/05 3/31/10, single PI, $435,000

2. Image Annotation and Retrieval, Google Inc., 9/05-8/07, single PI, $150,000 3. Interactive Neurotechnology for Image Analysis, DARPA, 8/1/05-8/1/06, co-pi, $75,000 4. Understanding Video of Crowded Environments, NSF, 11/15/05-11/15/08, single PI, $704,260 5. Sonar Image Enhancement and Classification, ONR, 9/25/06-9/25/07, co-pi, $137,874 6. Large-Vocabulary Semantic Image Processing: Theory and Algorithms, NSF, 9/1/08-8/31/11, single PI, $239,499 7. Optimal Automated Design of Cascaded Object Detectors, NSF, 9/1/08-8/31/11, single PI, $337,002 8. Motion-based Saliency Analysis for Event Detection and Video Summarization, Sony, 1/1/09-12/31/09, single PI, $67,500 9. Large-Vocabulary Semantic Image Processing: Theory and Algorithms, NSF, 8/7/09-8/31/11, PI, $402,062 10. Large-Vocabulary Semantic Image Processing: Theory and Algorithms, NSF, 9/15/10-8/31/12, PI, $206,139 11. Enabling Personalized Context Aware and Interactive Mobile Advertisements, CWC/UC Discovery, 11/01/09/10/31/11, Co-PI, $278,880 12. Saliency-based tracking, SONY, PI, $64,800 13. Large-Vocabulary Semantic Image Processing: Theory and Algorithms, NSF, 1/1/12-9/30/12, PI, $264,756 14. A Biologically Plausible Architecture for Robotic Vision, NSF, 12/09/01-17/08/31, single PI, $1,150,000 15. Pedestrian Detection, KETI, 5/10/12 6/1/17, $126,304 16. Large-Vocabulary Semantic Image Processing: Theory and Algorithms, NSF, 1/1/13-12/31/13, PI, $280,470 17. Large-Vocabulary Semantic Image Processing: Theory and Algorithms, NSF, 3/1/13-2/28/14, PI, $233,725

18. Beyond Features: Bayes-Optimal Hierarchical Networks that Exploit Vision Synergies, Samsung GRO, 1/1/14-12/31/14, PI, $60,000 19. Large-Vocabulary Semantic Image Processing: Theory and Algorithms, NSF, 4/1/14-9/30/14, PI, $234,225 20. Attention-Based Vision for Autonomous Vehicles, ONR, 5/5/14-4/30/15, PI, $50,000 21. NRI: Real-time Semantic Computer Vision for Co-Robotics, 9/1/2016-8/31/2019 STUDENTS PhD (current) Mandar Dixit Bo Liu Zhaowei Cai Pedro Morgado Yingwei Li PhD (graduated) Dashan Gao, 2008 Antoni Chan, 2008 Hamed Masnadi-Shirazi, 2011 Sunhyoung Han, 2011 Vijay Mahadevan, 2011 Nikhil Rasiwasia, 2011 Mohammed Saberian, 2014 Jose Costa-Pereira, 2015 Weixin Li, 2016 MS (graduated) Mulloy Murrow, 2012 Kritika Muralidharan, 2012 Song Lu, 2015 Si Chen, 2015 Can Xu, 2015 Post-Docs Guatavo Carneiro, 2004-2005 Antoni Chan, 2008-2009 Renaud Peteri, 2013

PUBLICATIONS Book Chapters 1. N. Vasconcelos, Statistical models of video structure and semantics, Ch. 3 in The Handbook of Video Databases, B. Furht and O. Marques, Eds. Boca Raton, FL, CPC Press, (2003), pp. 45-67. 2. A. Chan and N. Vasconcelos, Surveillance of Crowded Environments: Modeling the Crowd by its Global Properties, in Modeling, Simulation and Visual Analysis of Crowds, S. Ali, K. Nishino, D. Manocha, and M. Shah, Eds., Springer, pp. 265-389. Journal Papers 1. M. Pereira, N. Vasconcelos, and A. Alves, Frequency accommodation and synchronization in a digital TV codec, Electronics Letters, Vol. 26, No. 25 (December 6, 1990), pp. 2104-2105. 2. N. Vasconcelos and A. Lippman, Statistical models of video structure for content analysis and characterization, IEEE Transactions on Image Processing, Vol. 9, No. 1 (January 2000) pp. 3-19. 3. N. Vasconcelos and A. Lippman, Empirical Bayesian motion segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2 (February 2001), pp. 217-221. 4. N. Vasconcelos, On the efficient evaluation of probabilistic similarity functions for image retrieval, IEEE Transactions on Information Theory, Vol. 50, No. 7 (July 2004), pp. 1482-1496. 5. N. Vasconcelos, Minimum probability of error image retrieval, IEEE Transactions on Signal Processing, Vol. 52, No. 8 (August 2004), pp. 2322 2336. 6. N. Vasconcelos, Guest editorial: special issue in content based image and video retrieval, EURASIP Signal Processing Journal, Vol. 85, No. 2 (February 2005), pp. 231-232. 7. N. Vasconcelos and A. Lippman, A multi-resolution manifold distance for affine invariant recognition, IEEE Transactions on Multimedia, Vol. 7, No. 1 (February 2005), pp. 127-142

8. G. Carneiro, A. Chan, P. Moreno and N. Vasconcelos, Supervised Learning of Semantic Classes for Image Annotation and Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3, (March 2007), pp. 394-410. 9. N. Vasconcelos, From pixels to semantic spaces: advances in contentbased image retrieval, IEEE Computer, Vol. 40, No. 7 (July 2007), pp 20-26 10. N. Rasiwasia and N. Vasconcelos, "Bridging the gap: query by semantic example," IEEE Transactions on Multimedia, Vol. 9, No. 5, (August 2007), pp 923-938. 11. A. Chan and N. Vasconcelos, "Modeling, clustering, and segmenting video with mixtures of dynamic textures, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 5, (May 2008), pp. 909-926. 12. D. Gao, V. Mahadevan, and N. Vasconcelos, On the plausibility of the discriminant center-surround hypothesis for visual saliency, Journal of Vision, Vol. 8, No. 7, (2008), pp 1-18. 13. G. Carneiro and N. Vasconcelos, Minimum Bayes error features for visual recognition, Image and Vision Computing, Volume 1, 131-140, January 2009. 14. M. Vasconcelos and N. Vasconcelos, Natural Image Statistics and Lowcomplexity Feature Selection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, Issue 2 (February 2009), pp. 228-244. 15. D. Gao and N. Vasconcelos, Decision-theoretic saliency: computational principles, biological plausibility, and implications for neurophysiology and psychophysics, Neural Computation, Vol. 21 (January 2009), pp. 239-271. 16. T. Lin, L. Cervino, X. Tang, N. Vasconcelos, and S. Jiang, Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy, Physics in Medicine and Biology, Vol. 54 (February 2009), pp. 981-992. 17. D. Gao, S. Han, N. Vasconcelos, "Discriminant Saliency, the Detection of Suspicious Coincidences, and Applications to Visual Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 6, (June 2009), pp. 989-1005. 18. A. Chan and N. Vasconcelos, Layered Dynamic Textures, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, (October 2009), pp 1862-1879.

19. V. Mahadevan and N. Vasconcelos, Spatiotemporal Saliency in Dynamic Scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, (January 2010), pp 171-177. 20. N. Jacobson, Y-L. Lee, V. Mahadevan, N. Vasconcelos and T.Q. Nguyen, A Novel Approach to FRUC using Discriminant Saliency and Frame Segmentation, IEEE Transactions on Image Processing, Vol. 19, Issue 11 (November 2010), pp. 2924 2934 21. S. Han and N. Vasconcelos, Biologically Plausible Saliency Mechanisms Improve Feedforward Object Recognition, Vision Research, Vol. 50, Issue 22 (October 2010), pp. 2295-2307 22. H. Masnadi-Shirazi and N. Vasconcelos, Cost-Sensitive Boosting, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, Issue 2 (February 2011), 294-309 23. A. Chan, V. Mahadevan, and N. Vasconcelos, Generalized Stauffer- Grimson Background Subtraction for Dynamic Scenes, Journal of Machine Vision and Applications, vol. 22, Issue 5, 751-766, 2011 24. A. Chan and N. Vasconcelos, Counting People with Low-level Features and Bayesian Regression, IEEE Transactions on Image Processing, Vol. 21, Issue 4 (April 2012), 2160-2177 25. N. Rasiwasia and N. Vasconcelos, Holistic Context Models for Visual Recognition, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 34, Issue 5 (May 2012), 902-917 26. M. Saberian and N. Vasconcelos, Learning Optimal Embedded Cascades, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, Issue 10 (October 2012), 2005-2018 27. R. Kwitt, N. Vasconcelos, N. Rasiwasia, A. Uhl, B. Davis, M. Hafner and F. Wrba, Endoscopic image analysis in semantic space, Medical Image Analysis, vol. 16, Issue 7 (October 2012), 1415-1422 28. N. Vasconcelos and M. Vasconcelos, Minimum Probability of Error Image Retrieval: From Visual Features to Image Semantics, Foundations and Trends in Signal Processing, vol. 5, Issue 4, 265-389, 2012 29. V. Mahadevan and N. Vasconcelos, Biologically-inspired Object Tracking Using Center-surround Mechanisms, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 3 (March 2013), 541-554

30. R. Kwitt, N. Vasconcelos, S. Razzaque, and S. Aylward, Localizing Target Structures in Ultrasound Video - a Phantom Study, Medical Image Analysis, Vol. 17, Issue 7 (October 2013), 712-722 31. N. Rasiwasia and N. Vasconcelos, Latent Dirichelet Allocation Models for Image Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 11, (November 2013) 2665-2679 32. W. Li, V. Mahadevan and N. Vasconcelos, Anomaly Detection and Localization in Crowded Scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, Issue 1 (January, 2014) 18-32. 33. J. Costa Pereira, E. Coviello, G. Doyle, N. Rasiwasia, G. Lanckriet, R.Levy and N. Vasconcelos, On the Role of Correlation and Abstraction in Cross- Modal Multimedia Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, Issue 3, (March 2014), 521-535. 34. J. Costa Pereira and N. Vasconcelos, Cross-modal Domain Adaptation for Text-based Regularization of Image Semantics in Image Retrieval Systems, Computer Vision and Image Understanding, Vol. 124 (July 2014), 123-125. 35. M. Saberian and N. Vasconcelos, Boosting Algorithms for Detector Cascade Learning, Journal of Machine Learning Research, Vol. 15, Issue 5, (July 2014), 2569-2605. 36. S. Han and N. Vasconcelos, Object recognition with hierarchical discriminant saliency networks, Frontiers in Computational Neuroscience, Vol. 8 (September, 2014). 37. Z. Cai, L. Wen, Z. Lei, N. Vasconcelos, and S. Z. Li, Robust Deformable and Occluded Object Tracking with Dynamic Graph, IEEE Transactions on Image Processing, Vol 23 (December 2014), 5497-5509. 38. H. Masnadi-Shirazi and N. Vasconcelos, A View of Margin Losses as Regularizers of Probability Estimates, Journal of Machine Learning Research, Vol.16, (Dec. 2015), 2751-2795. 39. Y. Hong, R. Kwitt, N. Singh, N. Vasconcelos, and M. Niethammer, Parametric Regression on the Grassmannian, IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear. 40. W. Li and N. Vasconcelos, Complex Activity Recognition via Attribute Dynamics, International Journal of Computer Vision, to appear.

Conference Papers 1. N. Vasconcelos and A. Lippman, Library-based image coding, Int. Conf. Acoustics, Speech and Signal Processing, (Vol. V), Adelaide, Australia, April 19-22, 1994, pp. 489-492. 2. N. Vasconcelos and A. Lippman, Spatiotemporal model-based optical flow estimation, IEEE Int. Conf. Image Processing, (Vol. 2), Washington, DC, October 19-22, 1995, pp. 201-204. 3. N. Vasconcelos and A. Lippman, Frame-free video, IEEE Int. Conf. Image Processing, (Vol. 3), Lausanne, Switzerland, September 16-19, 1996, pp. 375-378. 4. N. Vasconcelos and A. Lippman, Library-based coding: a representation for efficient video compression and retrieval, IEEE Data Compression Conference, Snowbird, Utah, March 25-27, 1997, pp. 121-130. 5. N. Vasconcelos and A. Lippman, Empirical Bayesian EM-based motion segmentation, IEEE Conf. In Computer Vision and Pattern Recognition, San Juan, Puerto Rico, June 17-19, 1997, pp. 527-532. Acceptance rate: 11.4% 6. N. Vasconcelos and F. Dufaux, Pre and post-filtering for low bit-rate video coding, IEEE Int. Conf. Image Processing, (Vol. 1), Santa Barbara, California, October 10-14, 1997, pp. 291-294. 7. N. Vasconcelos and A. Lippman, Towards semantically meaningful feature spaces for the characterization of video content, IEEE Int. Conf. Image Processing, (Vol. 1), Santa Barbara, California, October 26-29, 1997, pp. 25-28. Invited 8. N. Vasconcelos and A. Lippman, Content-based pre-indexed video, IEEE Int. Conf. Image Processing, (Vol. 2), Santa Barbara, California, October 26-29, 1997, pp. 542-545. 9. N. Vasconcelos and A. Lippman, Multiresolution tangent distance for affineinvariant classification, Neural Information Processing Systems, Denver, Colorado, December 2-6, 1997, pp. 843-849. 10. N. Vasconcelos and A. Lippman, A Bayesian framework for content-based indexing and retrieval, IEEE Data Compression Conference, Snowbird, Utah, March 30 April 1, 1998, p. 580.

11. N. Vasconcelos and A. Lippman, A Bayesian framework for semantic content characterization, IEEE Conf. In Computer Vision and Pattern Recognition, Santa Barbara, California, June 13-15, 1998, pp. 566-571. 12. N. Vasconcelos and A. Lippman, A spatiotemporal motion model for video summarization, IEEE Conf. in Computer Vision and Pattern Recognition, Santa Barbara, California, June 23-25, 1998, pp. 361-366. Acceptance rate: 9.3% 13. N. Vasconcelos and A. Lippman, Bayesian modeling of video editing and structure: semantic features for video summarization and browsing, IEEE Int. Conf. Image Processing, (Vol. 3), Chicago, IL, October 4-7, 1998, pp. 153-157. 14. A. Lippman, N. Vasconcelos and G. Iyengar, Humane interfaces to video, 32nd Asilomar Conference on Signals, Systems, and Computers, (Vol. 1), Asilomar, CA, November 1-4, 1998, pp. 887-894. Invited 15. N. Vasconcelos and A. Lippman, Learning mixture hierarchies, Neural Information Processing Systems, Denver, Colorado, December 1-3, 1998, pp. 606-612. 16. N. Vasconcelos and A. Lippman, Learning from user feedback in image retrieval systems, Neural Information Processing Systems, Denver, Colorado, November 30 December 4, 1999, pp. 977-983. Acceptance rate: 6.4% 17. N. Vasconcelos and A. Lippman, A probabilistic architecture for contentbased image retrieval, IEEE Conf. in Computer Vision and Pattern Recognition, (Vol. 1), South Carolina, June 13-15, 2000, pp. 216-221. Acceptance rate: 14.2% 18. N. Vasconcelos and A. Lippman, Learning over multiple temporal scales in image databases, European Conference on Computer Vision, (Vol. 1), Dublin, Ireland, June 26 - July 1, 2000, pp. 33-47. Acceptance rate: 16.1% 19. N. Vasconcelos and A. Lippman, Feature representations for image retrieval: beyond the color histogram, International Conference on Multimedia and Expo, (Vol. 2), New York, July 30 August 2, 2000, pp. 899-902. 20. N. Vasconcelos and A. Lippman, A unifying view of image similarity, International Conference on Pattern Recognition, (Vol. 1), Barcelona, Spain, September 3-7, 2000, pp. 38-41.

21. N. Vasconcelos and A. Lippman, Bayesian video shot segmentation, Neural Information Processing Systems, Denver, Colorado, November 28-30, 2000, pp. 1009-1015. 22. N. Vasconcelos, On the complexity of probabilistic image retrieval, 8th International Conference on Computer Vision, (Vol. 2), Vancouver, Canada, July 7-14, 2001, pp. 400-407. Acceptance rate: 7.6% 23. N. Vasconcelos and M. Kunt, Content-based retrieval from image databases: current solutions and future directions, IEEE Int. Conf. Image Processing, (Vol. 3), Thessaloniki, Greece, October 17-25, 2001, pp. 6-9. Invited 24. N. Vasconcelos, ` Image indexing with mixture hierarchies, IEEE Conf. In Computer Vision and Pattern Recognition, (Vol. 1), Kauai, Hawaii, December 8 14, 2001, pp. 3-10. Acceptance rate: 8% 25. N. Vasconcelos and G. Carneiro, What is the role of independence for visual recognition? European Conference on Computer Vision, (Vol. 1), Copenhagen, Denmark, May 9-10, 2002, pp. 297-311. 26. N. Vasconcelos Decision-theoretic image retrieval, SPIE Photonics East, (Vol. 4862, special session on Statistical Multimedia Analysis), July 11-15, 2002, pp. 114-125. Invited 27. N. Vasconcelos, Exploiting group structure to improve retrieval accuracy and speed in image databases, Image Processing, 2002, Proceedings, 2002 International Conference, (Vol. 1), September 7-9, 2002, pp. 980-983. 28. N. Vasconcelos, Feature selection by maximum marginal diversity, Neural Information Processing Systems, Vancouver, Canada, December 3-5, 2002, pp. 1351-1358. Acceptance rate: 10.5% 29. P. Moreno, P. Ho and N. Vasconcelos, A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications, Neural Information Processing Systems, Vancouver, Canada, January 9, 2003, pp. 1385-1392. Acceptance rate: 9.1% 30. N. Vasconcelos, Feature selection by maximum marginal diversity: Optimality and implications for visual recognition, IEEE Conf. In Computer Vision and Pattern Recognition, (Vol. 1), Madison, Wisconsin, June 18-20, 2003, pp. 762-769. 31. N. Vasconcelos, The design of end-to-end optimal image retrieval systems, 13th International Conference on Artificial Neural Networks,

(Special Session on Neural Networks for Information Retrieval), Istanbul, Turkey, June 26-29, 2003. Invited 32. N. Vasconcelos, A family of information-theoretic algorithms for lowcomplexity discriminant feature selection in image retrieval, IEEE Int l. Conf. Image Processing, Barcelona, (Vol. 3), September 14-17, 2003, pp. 741-744. 33. N. Vasconcelos, P. Ho and P. Moreno, The Kullback-Leibler Kernel as a framework for discriminant and localized representations for visual recognition, Proc. European Conference on Computer Vision, (Vol. 3), Prague, Czech Republic, May 11-14, 2004, pp. 430-441. 34. N. Vasconcelos, M. Vasconcelos, Scalable discriminant feature selection for image retrieval and recognition, IEEE Conference on Computer Vision and Pattern Recognition, (Vol. 2), Washington DC, June 27 - July 2, 2004, pp. 770-775. 35. D. Gao and N. Vasconcelos, Discriminant saliency for visual recognition from cluttered scenes, Neural Information Processing Systems, Vancouver, Canada, December 13-18, 2004, pp. 481-488. Acceptance rate: 3% 36. G. Carneiro and N. Vasconcelos, Minimum bayes error features for visual recognition by sequential feature selection and extraction, Canadian Conference on Computer and Robot Vision, Vancouver, Canada, May 9-11, 2005, pp. 253-260. 37. A. Chan and N. Vasconcelos, Classification and retrieval of traffic video using auto-regressive stochastic processes, IEEE Intelligent Vehicles Symposium, Las Vegas, NV, June 6-8, 2005, pp. 771 776. 38. G. Carneiro and N. Vasconcelos, Formulating semantic image annotation as a supervised learning problem, IEEE Conf. In Computer Vision and Pattern Recognition, (Vol. 2), San Diego, CA, June 20-25, 2005, pp. 163-168. 39. D. Gao and N. Vasconcelos, Integrated learning of saliency, complex features, and object detectors from cluttered scenes, IEEE Conf. In Computer Vision and Pattern Recognition, (Vol. 2), San Diego, CA, June 20-25, 2005, pp. 282-287. 40. A. Chan and N. Vasconcelos, Probabilistic kernels for the classification of auto-regressive visual processes, IEEE Conf. In Computer Vision and Pattern Recognition, (Vol. 1), San Diego, CA, June 20-25, 2005, pp. 846-851. Acceptance rate: 6.5%

41. D. Gao and N. Vasconcelos, An experimental comparison of three guiding principles for the detection of salient image locations: stability, complexity, and discrimination, The 3rd International Workshop on Attention and Performance in Computational Vision (WAPCV), (Vol. 3), San Diego, CA, June 25, 2005, p. 84. 42. G. Carneiro and N. Vasconcelos, A database centric view of semantic image annotation and retrieval, ACM Conference on Research and Development in Information Retrieval (ACM SIGIR), Salvador, Brazil, August 3-6, 2005, pp. 559-566. 43. A. Chan and N. Vasconcelos, Mixtures of dynamic textures, IEEE International Conference on Computer Vision, (Vol. 1), Beijing, China, October 17-21, 2005, pp. 641 647. 44. A. Chan and N. Vasconcelos, Layered dynamic textures, Neural Information Processing Systems, Vancouver, Canada, December 5-9, 2005, pp. 203-210. 45. K. Ni, S. Kumar, N. Vasconcelos, and T. Nguyen, Single image superresolution based on support vector regression, IEEE International Conference on Acoustics, Speech, and Signal Processing, (Vol. 2), pp. 601-604, May 14-19, 2006. 46. M. Vasconcelos, N. Vasconcelos and G. Carneiro, Weakly supervised topdown image segmentation, IEEE Conf. In Computer Vision and Pattern Recognition, (Vol. 1), New York, NY, June 17-22, 2006, pp. 1001-1006. 47. N. Rasiwasia, N. Vasconcelos, and P.Moreno, Query by semantic example, International Conference on Image and Video Retrieval, to be held in Tempe, AZ, July 13-15, 2006, pp. 51-60. 48. A. Chan, P. Moreno, and N. Vasconcelos, Using statistics to search and annotate pictures, Proceedings of the Joint Statistical Meetings, Seattle, Washington, August 9-12, 2006. Invited 49. S. Han and N. Vasconcelos, Image compression using object-based regions of interest, IEEE International Conference on Image Processing (ICIP), to be held in Atlanta, GA, October 8-11, 2006, 4 pages. 50. D. Gao and N. Vasconcelos, Discriminant interest points are stable, IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, June 18-23, 2007, 6 pages

51. A.B. Chan and N. Vasconcelos, Classifying video with kernel dynamic textures, IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, June 18-23, 2007, 6 pages 52. A.B. Chan, N. Vasconcelos, and G.R.G. Lanckriet, Direct convex relaxations of sparse SVM, International Conference on Machine Learning, Corvallis, Oregon, June 20-24, 2007, 8 pages 53. H. Masnadi-Shirazi, N. Vasconcelos, Asymmetric boosting, International Conference on Machine Learning, Corvallis, Oregon, June 20-24, 2007, 8 pages. 54. D. Gao and N. Vasconcelos, Bottom-up Saliency is a Discriminant Process, IEEE International Conference on Computer Vision, Rio de Janeiro, Brasil, 2007, 6 pages 55. H. Masnadi-Shirazi, N. Vasconcelos, High Detection-Rate Cascades for Real-Time Object Detection, IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007, 6 pages 56. D. Gao, V. Mahadevan, and N. Vasconcelos, The Discriminant Centersurround Hypothesis for Bottom-up Saliency, Neural Information Processing Systems, Vancouver, 2007, 8 pages 57. S. Han and N. Vasconcelos, Object-based regions of interest for image compression, Data Compression Conference (DCC), pp 132-141, Snowbird, Utah, 2008. 58. A. B. Chan, Z. S. J. Liang, and N. Vasconcelos, Privacy Preserving Crowd Monitoring: Counting People without People Models or Tracking, IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, 2008, 7 pages. 59. N. Rasiwasia and N. Vasconcelos, Scene Classification with Lowdimensional Semantic Spaces and Weak Supervision, IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, 2008, 6 pages. 60. V. Mahadevan and N. Vasconcelos, Background Subtraction in Highly Dynamic Scenes, IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, 2008, 6 pages. 61. T. Lin, L. Cervino, X. Tang, N. Vasconcelos, and S. Jiang, Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques, IEEE International Conference on Machine Learning and Applications, San Diego, California, 2008, pp: 533 538.

62. N. Rasiwasia and N. Vasconcelos, A Systematic Study of the role of Context on Image Classification, IEEE International Conference on Image Processing (ICIP), San Diego, California, Oct. 2008. 63. S. Han and N. Vasconcelos, Complex discriminant features for object classification, IEEE International Conference on Image Processing (ICIP), San Diego, California, Oct. 2008. (best student paper award) 64. H. Masnadi-Shirazi and N. Vasconcelos, On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost, Neural Information Processing Systems (NIPS), Vancouver, Canada, 2008, 8 pages. 65. A. Chan, M. Morrow, and N. Vasconcelos, Analysis of Crowded Scenes using Holistic Properties, 11th IEEE Intl. Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2009), Miami, June 2009 66. A. Chan and N. Vasconcelos, Variational Layered Dynamic Textures, IEEE Conference on Computer Vision and Pattern Recognition, Miami, June 2009. Acceptance rate: 4.2% 67. N. Rasiwasia and N. Vasconcelos, Holistic Context Modeling using Semantic Co-occurrences, IEEE Conference on Computer Vision and Pattern Recognition, Miami, June 2009. 68. V. Mahadevan and Vasconcelos Saliency-based Discriminant Tracking, IEEE Conference on Computer Vision and Pattern Recognition, Miami, June 2009. Acceptance rate: 4.2% 69. A. Chan and N. Vasconcelos, Bayesian Poisson Regression for Crowd Counting, IEEE International Conference on Computer Vision, Kyoto, September 2009, pp. 545-551 70. V. Mahadevan, W. Li, V. Bhalodia and N. Vasconcelos, Anomaly Detection in Crowded Scenes, IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 1975-1981. Acceptance rate: 5% 71. H. Masnadi-Shirazi, V. Mahadevan and N. Vasconcelos, On the Design of Robust Classifiers for Computer Vision, IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 779-786. Acceptance rate: 5% 72. H. Masnadi-Shirazi and N. Vasconcelos, Risk minimization, probability elicitation, and cost-sensitive SVMs, International Conference on Machine

Learning (ICML), June 2010, 8 pages 73. N. Jacobson, Y-L. Lee, V. Mahadevan, N. Vasconcelos and T.Q. Nguyen, Motion Vector Refinement for FRUC Using Saliency and Segmentation, IEEE International Conference on Multimedia & Expo, Singapore, Jul 2010, pp. 778-783 74. N. Rasiwasia, J. Costa Pereira, E. Coviello, G. Doyle, G.R.G. Lanckriet, R. Levy, N. Vasconcelos, A New Approach to Cross-Modal Multimedia Retrieval, ACM International Conference on Multimedia, Florence, Italy, Oct 2010, pp. 251-260. (best student paper award) 75. H. Masnadi-Shirazi and N. Vasconcelos, Variable margin losses for classifier design, Neural Information Processing Systems, Vancouver, Canada, Dec 2010, 9 pages 76. K. Muralidharan and N. Vasconcelos, A biologically plausible network for the computation of orientation dominance, Neural Information Processing Systems, Vancouver, Canada, Dec 2010, 9 pages 77. M. Saberian and N. Vasconcelos, Boosting classifier cascades, Neural Information Processing Systems, Vancouver, Canada, Dec 2010, 9 pages 78. M. Saberian, H. Masnadi-Shirazi and N. Vasconcelos, TaylorBoost: First and Second Order Boosting Algorithms with Explicit Margin Control, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, 2011 79. M. Dixit, N. Rasiwasia and N. Vasconcelos, Adapted Gaussian Models for Image Classification, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, 2011 80. R. Kwitt, N. Rasiwasia, N. Vasconcelos, A. Uhl, M. Hafner, F. Wrba, Learning Pit Pattern Concepts for Gastroenterological Training, International Conference on Medical Image Computing and Computer Assisted Intervention, Toronto, September 2011 81. V. Mahadevan, C-W. Wong, J Costa-Pereira, T.T. Liu, N. Vasconcelos and L.K. Saul, Maximum Covariance Unfolding - Manifold Learning for Bimodal Data, Neural Information Processing Systems, Granada, Spain, December 2011 82. M. Saberian and N. Vasconcelos, Multiclass Boosting: Theory and Algorithms, Neural Information Processing Systems, Granada, Spain, December 2011.

83. M. Saberian and N. Vasconcelos, Boosting Algorithms for Simultaneous Feature Extraction and Selection, IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2012. 84. J. Costa-Pereira, and N. Vasconcelos, On the Regularization of Image Semantics by Modal Expansion, IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2012 85. R. Kwitt, N. Vasconcelos, S. Razzaque and S. Aylward, Recognition in Ultrasound Videos: Where am I?, International Conference on Medical Image Computing and Computer Assisted Intervention, (Young Scientist award), Nice, 2012 86. R. Kwitt, N. Vasconcelos, N. Rasiwasia, Scene Recognition on the Semantic Manifold, IEEE European Conference on Computer Vision, Firenze, Italy, 2012 87. W. Li and N. Vasconcelos, Recognizing Activities by Attribute Dynamics, Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, United States, 2012 88. V. Mahadevan and N. Vasconcelos, On the connections between saliency and tracking, Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, United States, 2012 89. W. Li, Q. Yu, H. Sawhney and N. Vasconcelos, Recognizing Activities via Bag of Words for Attribute Dynamics, IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, United States, 2013 90. W. Li, Q. Yu and N. Vasconcelos, Dynamic Pooling for Complex Event Recognition, IEEE International Conference on Computer Vision, Sydney, New South Wales, Australia, 2013 91. N. Rasiwasia, M. Dixit and N. Vasconcelos, Class-Specific Simplex-Latent Dirichlet Allocation for Image Classification, IEEE International Conference on Computer Vision, Sydney, New South Wales, Australia, 2013 92. S. Lu, V. Mahadevan and N. Vasconcelos, Learning optimal seeds for diffusion-based salient object detection, IEEE Conference on Computer Vision and Pattern Recognition, Cleveland, 2014. 93. C. Xu and N. Vasconcelos, Learning Receptive Fields for Pooling from Tensors of Feature Response, IEEE Conference on Computer Vision and Pattern Recognition, Cleveland, 2014

94. M. Saberian, O. Beijbom, David Kriegman, and N. Vasconcelos, Guess- Averse Loss Functions For Cost-Sensitive Multiclass Boosting, International Conference on Machine Learning, Beijing, China, 2014. 95. Y. Hong, R. Kwitt, N. Singh, B. Davis, N. Vasconcelos, and M. Niethammer, Geodesic Regression on the Grassmannian, European Conference in Computer Vision, Zurich, Switzerland, 2014. 96. M. Saberian and N. Vasconcelos, Multi-Resolution Cascades for Multiclass Object Detection, Advances in Neural Information Processing Systems, Quebec, Canada, 2014. 97. S. H. Khatoonabadi, N. Vasconcelos, I. V. Bajic, and Y. Shan, How many bits does it take for a stimulus to be salient?, IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, 2015. (Oral Presentation, acceptance rate: 3.3%) 98. W. Li and N. Vasconcelos, Multiple Instance Learning for Soft Bags via Top Instances, IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, 2015. 99. M.Dixit, S. Chen, D. Gao, N. Rasiwasia and N. Vasconcelos, Scene Classification with Semantic Fisher Vectors, IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, 2015. 100. B. Liu, N. Vasconcelos, Bayesian Model Adaptation for Crowd Counts, IEEE International Conference on Computer Vision, Santiago, Chile, 2015. 101. P. Jiang, N. Vasconcelos, J. Peng, Generic Promotion of Diffusion-Based Salient Object Detection, IEEE International Conference on Computer Vision, Santiago, Chile, 2015. 102. Z. Cai, M. Saberian and N. Vasconcelos, Learning Complexity-Aware Cascades for Deep Pedestrian Detection, IEEE International Conference on Computer Vision, Santiago, Chile, 2015. (Oral Presentation, acceptance rate: 4%). 103. F. Vasconcelos and N. Vasconcelos, Person-Following UAVs, IEEE Winter Conference on Applications of Computer Vision, Lake Placid, New York, 2016. 104. Y. Li, W. Li, V. Mahadevan, and N. Vasconcelos, VLAD3: Encoding Dynamics of Deep Features for Action Recognition, IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, 2016.

105. X. Zhao, X. Liang, L. Liu, T. Li, Y. Lan, N. Vasconcelos, and S. Yan, Peak- Piloted Deep Network for Facial Expression Recognition, European Conference in Computer Vision, Amsterdam, Netherlands, 2016. 106. Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos, A Unified Multi-Scale Deep Convolutional Neural Network for Fast Object Detection, European Conference in Computer Vision, Amsterdam, Netherlands, 2016. 107. M. George, M. Dixit, G. Zogg, and N. Vasconcelos, Semantic Clustering for Robust Fine-Grained Scene Recognition, European Conference in Computer Vision, Amsterdam, Netherlands, 2016. 108. M. Moghimi, M. Saberian, J. Yang, L. Ji, N. Vasconcelos, and S. Belongie, Boosted Convolutional Neural Networks, British Machine Vision Conference, York, UK, 2016. 109. M. Saberian, J. Costa-Pereira, C. Xu, and N. Vasconcelos, Large Margin Discriminant Dimensionality Reduction in Prediction Space, Advances in Neural Information Processing Systems, Barcelona, Spain, 2016. 110. M. Dixit and N. Vasconcelos, Object-Based Scene Representations Using Fisher Scores of Local Subspace Projections, Advances in Neural Information Processing Systems, Barcelona, Spain, 2016. 111. P. Morgado and N. Vasconcelos, Semantically Consistent Regularization for Zero-Shot Recognition, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, 2017. 112. Z. Cai, X. He, J. Sun, and N. Vasconcelos, Deep Learning with Low- Precision by Half-Wave Gaussian Quantization, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, 2017. 113. M. Dixit, R. Kwitt, M. Niethammer, and N. Vasconcelos, AGA: Attribute Guided Augmentation, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, 2017. (Oral Presentation, acceptance rate: 4%).

Michael C. Yip Assistant Professor of Electrical and Computer Engineering Jacobs School of Engineering, University of California San Diego (UCSD) Email: yip@.uscd.edu Website: http://www.ucsdarclab.com Updated July 27, 2017 RESEARCH THEMES Artificial intelligence and autonomous control, learning biomimetic robot manipulation, snake-like continuum robot design and control, image guided surgery, artificial muscle-powered machines EDUCATION Ph.D., Bioengineering, Stanford University, USA 2015 Dissertation: Model-less Control for Flexible Robotic Manipulators M.A.Sc., Electrical Engineering, University of British Columbia, Canada 2011 Dissertation: Ultrasound Registration and Tracking for Robot-Assisted Surgery B.A.Sc., Mechatronics Engineering, University of Waterloo, Canada 2009 ACADEMIC POSITIONS University of California, San Diego, La Jolla, CA, USA 2016 - Assistant Professor, Electrical and Computer Engineering Affiliate Faculty, Mechanical and Aerospace Engineering Director, Advanced Robotics and Controls Lab Stanford University, Stanford, California, USA 2011-2016 Research Assistant, Camarillo Lab and Salisbury Lab, BioX Fellow, NSERC Fellow Walt Disney Imagineering, Los Angeles, CA, USA 2014 Research Associate, Disney Research University of British Columbia, Vancouver, BC, Canada 2009-2011 Research Assistant, Robotics and Controls Group Harvard University, Cambridge, MA, USA 2008 Research Assistant, Harvard BioRobotics Lab Massachusetts Institute of Technology, Cambridge, MA, USA 2007 2008 Research Assistant, Robert S. Langer Lab Harvard Medical School, Cambridge, MA, USA 2007 Research Assistant, Center for Laryngeal Surgery & Vocal Rehab. University of Waterloo, Waterloo, ON, Canada 2005-2007 Undergraduate Research Assistant, Systems Design and Mechanical Engineering TEACHING University of California, San Diego, Electrical & Computer Engineering Department Instructor, Advances in Robot Manipulation, ECE285 2017-

Instructor, Fast Prototyping, ECE115 2016- Mentor, Senior Design Projects, ECE191 (3x), BIOE187A-D, MAE156A-B 2016- Stanford University, Bioengineering Department Teaching Assistant, Optics and Devices Laboratory (BIOE123) 2014-2015 Teaching Assistant, Biodesign Project (BIOE141) 2013 2014 Teaching Assistant, Introduction to Bioengineering Research (MED289) 2012-2014 University of British Columbia, Electrical & Computer Engineering Dept. Teaching Assistant, Elec. & Comp. Engineering Lab I, EECE280 2010 PUBLICATIONS 2017 M. Yip, N. Das, Robot Autonomy for Surgery. Encyclopedia of Medical Robotics, Vol. 4, World Scientific Publishing, 2017. To Appear. J. Zhang, A. Simeonov, M. Yip, Three-Dimensional Hysteresis Modeling of Robotic Artificial Muscles with Application to Shape Memory Alloy Actuators, Proc. Robotics, Science and Systems 2017. Boston, MA. 8 pages. M. Yip and G. Niemeyer, On Control and Properties of Super-coiled Polymer Actuators. IEEE Transactions on Robotics, vol. 33, no. 3, pp. 689-699. M. Yip and J. Forsslund, Spurring Innovation in Spatial Haptics: How Open-Source Hardware Can Turn Creativity Loose. IEEE Robotics and Automation Magazine, vol. 24, no. 1, pp. 65-78, 2017. J. Zhang, K. Iyer, A. Simeonov and M. C. Yip, "Modeling and Inverse Compensation of Hysteresis in Supercoiled Polymer Artificial Muscles." IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 773-780, 2017. 2016 J. Zhang and M. Yip, Designing Muscle-Powered Robotics using Super-coiled Polymers Robotics: Science and Systems. In workshop: Robot Makers: The future of digital rapid design and fabrication of robots. June 18-21, 2016. Ann-Arbor, Michigan. M. Yip, J. Sganga, and D. Camarillo, Model-less Control of Continuum Manipulators for Complex, Constrained Cardiac Ablation Tasks. Journal of Medical Robotics Research. 1750002, 2016. M. Yip and D. Camarillo, Model-less Hybrid Position/Force Control: A Minimalist Approach to Continuum Manipulator Control in Unknown Environments. IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 844-851, 2016. Best paper award for RA-L 2016 (of over 450 published). S. Padmaja, P. Jonnalagedda, F. Deng, K. Douglas, L. Chukoskie, M. Yip, T.N. Ng, T. Nguyen, A. Skalsky, H. Garudadri, An Instrumented Glove for Improving Spasticity Assessment. IEEE-NIH 2016 Special Topics Conference on Healthcare Innovations and Point-of-Care Technologies. Nov 9-11, Cancun, Mexico. 4 pages. 2016.

2015 M. Yip and G. Niemeyer, High-Performance Robotic Muscles from Conductive Nylon Sewing Thread. IEEE International Conference on Robotics and Automation - ICRA2015. May 26-30, 2015. Seattle, WA. pp. 2313-2318. Best paper award finalist. J. Forsslund, M. Yip and E-L. Sallnas, WoodenHaptics: A Starting Kit for Crafting Force- Reflecting Spatial Haptic Devices. Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction - TEI2015. pp. 133-140. ACM, 2015. 2014 M. Yip and D. Camarillo, Model-less Feedback Control of Continuum Manipulators in Constrained Environments. IEEE Transactions on Robotics, vol. 30, no. 4, pp. 880-889, 2014. M. Yip, P. Wang, and D. Camarillo, Model-less Control of a Flexible Robotic Catheter. Advances in Flexible Robotics for Medical Applications. Proceedings from IEEE International Conference on Robotics and Automation - ICRA2014, May 31-June 7, 2014. Hong Kong. Best paper award. F. Hernandez, L. Wu, M. Yip, A. Hoffman, J. Lopez, G. Grant, S. Kleiven and D. Camarillo, Six Degree of Freedom Measurements of Human Mild Traumatic Brain Injury. Annals of Biomedical Engineering. Article ID s10439-014-1212-4. pp. 1-16, 2014. M. Yip*, L. Wu*, F. Hernandez, J. Schooler, K. Bui, B. Hammoor, E. Ortega, G. Yock, J. Lopez, A. Hoffman, G. Grant and D. Camarillo, Prediction of Human Mild Traumatic Brain Injury in Multiple Dimensions. World Congress on Biomechanics. July 6-11, 2014. Boston, Massachussetts. (*co-first author) F. Hernandez, L. Wu, M. Yip and D. Camarillo, Linear and Rotational Measurements of Human Mild Traumatic Brain Injury. Tenth World Congress on Brain Injury, March 19-22, 2014, San Francisco, California. F. Hernandez, L. Wu, M. Yip, A. Hoffman, J. Lopez, G. Grant, S. Kleiven and D. Camarillo, Finite Element Simulation of Brain Deformation from Six Degree of Freedom Acceleration Measurements of Mild Traumatic Brain Injury. National Neurotrauma Society, Jun 29-Jul 2, 2014, San Francisco, California. 2013 O. Mohareri, C. Schneider, T. Adebar, M. Yip, P. Black, C. Nguan, D. Bergman, J. Seroger, S. DiMaio and S. Salcudean, Ultrasound-Based Image Guidance for Robot-Assisted Laparoscopic Radical Prostatectomy: Initial in-vivo results. Proceedings from of Information Processing for Computer-Assisted Intervention - IPCAI 2013, Lecture Notes in Computer Science, no. 7915, pp. 40-50, 2013. 2012 M. Yip, D. Lowe, S. Salcudean, R. Rohling and C. Nguan, Tissue Tracking and Registration for Image-Guided Surgery. IEEE Transactions on Medical Imaging, vol. 31, no. 11, pp. 2169-2182, 2012.

T. Adebar, M. Yip, S. Salcudean, R. Rohling, C. Nguan and L. Goldenberg, Registration of 3D Ultrasound through an Air Tissue Boundary. IEEE Transactions on Medical Imaging, vol. 31, no. 11, pp. 2133-2142, 2012. M. Yip, D. Lowe, S. Salcudean, R. Rohling and C. Nguan, Real-time Methods for Long-Term Tissue Feature Tracking in Endoscopic Scenes. Proceedings from of Information Processing for Computer-Assisted Intervention - IPCAI 2012, pp. 33-43. 2011 M. Yip, M. Tavakoli and R. Howe, Performance Analysis of a Haptic Telemanipulation Task under Time Delay. Advanced Robotics, vol. 25, no. 5, pp. 651-673, 2011. 2010 M. Yip, S. Yuen and R. Howe. A Robust Uniaxial Force Sensor for Minimally Invasive Surgery. IEEE Transactions on Biomedical Engineering. vol. 57, no. 5, pp. 1008-1011, 2010. M. Yip, T. Adebar, R. Rohling, S. Salcudean and C. Nguan. 3D Ultrasound to Stereoscopic Camera Registration through an Air-Tissue Boundary. Proceedings from Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, Lecture Notes in Computer Science, no. 6362, pp. 626-634, 2010. M. Yip, M. Tavakoli and R. Howe, Performance Analysis of a Manipulation Task in Time- Delayed Teleoperation. Proceedings from IEEE/RSJ International Conference on Intelligent Robots and Systems - IROS 2010, pp. 5270-5275, 2010. H. Park, M. Yip, J. Kost, J. Kobler, R. Langer, S. Zeitels and B. Chertok. Indirect Low- Intensity Ultrasonic Stimulation for Tissue Engineering. Journal of Tissue Engineering, Article ID 973530, 2010. 9 pages. 2009 S. Yuen, M. Yip, N. Vasilyev, D. Perrin, P. del Nido and R. Howe, Robotic Force Stabilization for Beating Heart Intracardiac Surgery. Proceedings from Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009, Lecture Notes in Computer Science, no. 5761, pp. 26-33, 2009. 2007 M. Ottensmeyer, M. Yip, C. Walsh, J. Kobler, J. Heaton, and S. Zeitels. Intra-operative Laryngoscopic Instrument for Characterizing Vocal Fold Viscoelasticity. Proceedings of ASME BioMed2007, Irvine, CA. pp. 133-134, 2007. Patents M. Yip, A. Gunn, P. Weissbrod, MultiCatheter Flexible Robotic System, U.S. Provisional Patent 62/515,762. Jun 6, 2017. M. Yip, System and Method for Endoscope Locomotion and Shaping, U.S. Provisional Patent 62/485,031. Apr. 13, 2017. M. Yip, J. Zhang, A. Tran, W. Kuang, System and Method for Robust And Low-Cost Multi- Axis Force Sensor. U.S. Provisional Patent 62/433,578. Dec 16, 2016.

M. Kurt, M. Yip, C. Kuo, M. Fanton, D. Camarillo, Impact protection system by controlling pressure release. U.S. Provisional Patent 62/428,844. Dec. 1, 2016. M. Yip and D. Camarillo, Position/Force Control of a Flexible Robot under Model-less Control. U.S. Patent Application 15/523,648. Provisional Date: Nov. 3, 2014. M. Yip and D. Camarillo, Model-less Control for Continuum and Redundant Robots. U.S. Patent 9,488,971. Nov. 8, 2016. S. Salcudean, M. Yip, T. Adebar, R. Rohling and C. Nguan A Method for 3D Ultrasound to Stereoscopic Camera Registration through an Air-Tissue Boundary. U.S. Patent Application 2012 0071757 A1. Mar. 22, 2011. INVITED TALKS IROS2017, Two Invited Talks Continuum Robots in Medicine: Design, Integration, and Applications Workshop. Topic: Continuum Robots in Medicine. Sep 24, 2017. Workshop on Medical Imaging Robotics. Topic: Image-guided Robots in Medicine. Sep 28, 2017. UCSD Cymer Dynamics and Controls Seminar. Topic: Surgical Robotic. Jun 9, 2017. UC Robotic Surgery Symposium. San Diego, CA. Topic: Surgical Robotics and Augmented Reality for Surgery. May 19, 2017. UCSD Bioengineering Seminar. San Diego, CA. Topic: Surgical Robotics, Visual Computation and Artificial Muscles. May 4, 2017. UC Riverside Bioengineering Colloquium. Riverside, CA. Topic: Surgical Robotics, Visual Computation and Artificial Muscles. Apr. 26, 2017. UCSD Institute of Neural Computing. San Diego, CA. Topic: Autonomous Surgery. Mar 03, 2017. NASA Jet Propulsion Lab. Pasedena, CA. Distinguished Lecturer Seminar Series on Medical Engineering. Topic: Surgical Robotics, Visual Computation and Artificial Muscles. Nov. 17, 2016. SRI. Menlo Park, CA. Topic: Surgical Robotics, Visual Computation and Artificial Muscles. Oct. 25, 2016. Think Surgical. Fremont, CA. Topic: Surgical Robotics. Oct. 11, 2016. BOSCH. Palo Alto, CA. Topic: Visual Computation and Augmented Reality. Oct. 10, 2016. Intuitive Surgical Inc. Sunnyvale, CA. Topic: Surgical Robotics. Oct. 06, 2016. Disney Research, Los Angeles, CA. Topic: Artificial Muscles. Jun. 27, 2016. RSS: Robotics: Science and Systems, In workshop: Robot Makers: The future of digital rapid design and fabrication of robots. Ann-Arbor, Michigan. Jun 19, 2016. Qualcomm Research, San Diego, CA. Topic: Surgical Robotics, Visual Computation, and Artificial Muscles. Dec. 14, 2015. Biomimetics, Artificial Muscle, and Nano-bio (BAMN2015), Vancouver, BC, Canada. Aug. 24-26, 2015. U.C. San Diego, San Diego, CA. host: Electrical & Computer Engineering Department. Mar 25, 2015. McGill University, Montreal, Canada. host: Mechanical Engineering Department. Mar 16, 2015. Massachusetts Institute of Technology, Cambridge, MA. co-hosts: Mechanical Engineering Department and Institute for Medical Engineering and Sciences. Feb 18, 2015. Princeton University, Princeton, NJ. host: Mech. & Aerospace Engineering Department. Feb 11, 2015. Tesla Motors, Palo Alto, CA. Feb 9, 2015. Carnegie Mellon University, Pittsburgh, PA. host: Mechanical Engineering Department. Feb 5, 2015.

Stanford School of Medicine, Research in Bioengineering Seminar, Stanford, CA. host: Dr. Paul Wang. Dec 3, 2014. Stanford University, Stanford Robotics Seminar, Stanford, CA. Oct 24, 2014. U.C. Berkeley, Berkeley Robotics Group, Berkeley, CA. host: Dr. Pieter Abbeel. Sep 10, 2014. Disney Research, Walt Disney Imagineering, Glendale, CA. Apr 10, 2014. Auris Surgical Robotics, Redwood City, CA. Nov 22, 2013. Stanford School of Medicine, Research in Bioengineering Seminar, Stanford, CA. Nov 14, 2013. Stanford University, Stanford Robotics Seminar, Stanford, CA. May 3, 2013. SELECT NEWS COVERAGE TheRegister.co.uk. Robo-surgeons, self-driving cars face similar legal, ethical headaches. Jul 13, 2017. +Reuters. Smart glove could help measures muscle stiffness. May 12, 2017. IEEE Spectrum. How Open-Source Robotics Hardware Is Accelerating Research and Innovation. Mar 23, 2017. +Medical Design Technology. Engineers Developing Advanced Robotic Systems That Will Become Surgeon's Right Hand. Jan 10, 2016. UCSD News. Engineering students try to become pinball wizards in this class. Jul. 7, 2016. UCSD News. UC San Diego s Big Ideas for 2016 and Beyond. Jan. 7, 2016. San Diego Business Journal. From Commercial to Defense, Region Welcomes Robots. Dec. 29, 2015. Popular Mechanics. Disney uses Fabric Store Supplies to build Artificial Muscles. Jun. 10, 2015. Gizmodo. Regular Old Sewing Thread Can Be Woven Into Strong Artificial Muscles. Jun. 10, 2015. Hackaday. Open Source Haptics Kit Aims to Democratize Force Feedback. Jan. 21, 2015. + indicates one of several news outlets AWARDS Hellman Fellowship 2017 Best Paper Award for 2016 (of 450 published), IEEE Robotics and Automation Letters 2017 Best Paper Finalist, Int. Conf. on Robotics and Automation (ICRA) 2015 Best Paper Award, Int. Conf. on Robotics and Automation (ICRA) 2014 for Advances in Flexible Robotics for Medical Interventions Stanford BioX Bowes Fellowship 2013-2016 NSERC National Post-Graduate Scholarship 2011-2014 Stanford Bioengineering Fellowship 2011-2013 Graduate Entrance Scholarship, University of British Columbia 2009 OEIO Scholarship (held at Harvard University) 2008 Queen Elizabeth II s Aiming for the Top Scholarship 2004-2009 Undergraduate Research Assistantship Scholarships 2005, 2006, 2007 Chandrashekar/Shad Valley Memorial Engineering Scholarship 2004 PROFESSIONAL SERVICE Program Committee: Robotics, Science, and Systems (RSS), Primary Area Chair 2017 IEEE Haptics Symposium, Sponsorship Chair 2017-2018

Associate Editor: IEEE International Conference on Robotics and Automation (ICRA) 2015-2017 Reviewer: Conference on Robot Learning (CoRL) 2017 - International Journal of Robotics Research (IJRR) 2015 - IEEE Transactions on Robotics (T-RO) 2011 - IEEE Int. Conf. on Robotics and Automation (ICRA) 2011 - International Journal of Computer Assisted Radiology and Surgery (IJCARS) 2015 - Bioinspiration and Biomimetics 2015 - Soft Robotics 2015 - IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) 2011- IEEE Transactions on Biomedical Engineering (T-BME) 2011-2012 Advanced Robotics 2014-2015 ASME Journal of Mechanisms and Robotics 2011, 2016 Human Robot Interaction 2016 - IEEE Transactions on Mechatronics 2016 - Other Service: NSF Panel Reviewer National Robotics Initiative 2.0 2017 Contributing Author, Hilbert Problems in Robotics 2017 International committee of leading robotics experts to set the Hilbert problems for robotics. Contributing Author, US Congressional Robotics Roadmap 2016 Expert author in defining surgical robotics outlook in US for 5, 10, 15 year for advising federal funding in the field of robotics. Workshops: Co-Chair, Continuum Robots in Medicine Design, Integration, and Applications. IROS2017 full-day workshop; 16 invited talks, posters. Co-Chair, C 4 Surgical Robots: Compliant, Continuum, Cognitive, and Collaborative. ICRA2017 full-day workshop; 21 invited talks, posters, JMRR follow-up publications. Southern California Robotics Symposium, 2017. UCSD ARClab Showcase. Southern California Robotics Symposium, 2016. UCSD ARClab Showcase.

COPY FROM 2017 18 UC SAN DIEGO GENERAL CATALOG Interim Update (November 1, 2017) Electrical and Computer Engineering ECE (graduate curriculum) Electrical and Computer Engineering (ECE) [ undergraduate program courses faculty ] Earl Warren College Jacobs Hall Undergraduate Affairs, Room 2906 Graduate Affairs, Room 2718 http://www.ece.ucsd.edu/ All courses, faculty listings, and curricular and degree requirements described herein are subject to change or deletion without notice. The Graduate Programs Master of Science The ECE department offers MS programs in electrical and computer engineering. The MS programs are research oriented and are intended to provide the intensive technical preparation necessary for advanced technical work in the engineering profession or subsequent pursuit of a PhD. The MS may be earned either with a thesis (Plan 1) or with a comprehensive examination (Plan 2). However, continuation in the PhD program requires a comprehensive examination so most students opt for Plan 2. Course Requirements: The total course requirements for the master of science degrees in electrical engineering and computer engineering are forty-eight units (twelve quarter courses), respectively, of which at least thirty-six units must be in graduate courses. Note that this is greater than the minimum requirements of the university. The department maintains a list of core courses for each disciplinary area from which the thirty-six graduate course units must be selected. The current list may be obtained from the department graduate office or the official website of the department. Students in interdisciplinary programs may select other core courses with the approval of their academic adviser. The course requirements must be completed within two years of full-time study. Students will be assigned a faculty adviser who will help select courses and approve their overall academic curriculum. Degree Requirements: Only upper-division (100-level) and graduate (200-level) courses in which a student is assigned grades A, B, or C (including plus [+] or minus [ ]) can be used to satisfy degree requirements. The fortyeight units of required course work must be taken for a letter grade, except in cases where students take ECE 299, the ECE graduate research course. Graduate research courses in the Irwin and Joan Jacobs School of Engineering may be counted toward the degree. Graduate research courses from other departments may be counted toward the degree with curriculum adviser approval. Graduate research courses (i.e., ECE 299) are the only courses where the S/U grading option is allowed. Students must maintain a GPA of 3.0 overall. Thesis and Comprehensive Requirements: The department offers both MS Plan 1 (thesis) and MS Plan 2 (written comprehensive exam). Students in the MS program must select either Plan 1 or Plan 2 by their fourth quarter of study. Students in the MS Plan 1 (thesis) must take twelve units of ECE 299 (Research) and must submit a thesis as described in the general requirements of the university. Students in the MS Plan 2 (written comprehensive exam) may count four units of ECE 299 (Research) toward the thirty-six graduate units required and must attempt to pass the departmental written comprehensive examination not later than the fall quarter of their second year of study. Students who pass the written examination at the MS level will receive a terminal master s degree, if they do not already have one.

COPY FROM 2017 18 UC SAN DIEGO GENERAL CATALOG Interim Update (November 1, 2017) Electrical and Computer Engineering ECE (graduate curriculum) Transfer to the PhD Program: Students in the MS program wishing to be considered for admission to the PhD program should consult their academic adviser as soon as possible. Students must notify department of their intent to transfer by their fourth quarter of study. Transfer from the MS to the PhD program is possible provided that the student Satisfy all requirements for initial admission to the PhD program, including submission of GRE general test scores, and be approved for consideration for transfer to the PhD program by the ECE Graduate Admissions Committee. Identify a faculty member who agrees, in writing, to serve as that student s academic and PhD research adviser. In consultation with the academic adviser, design and complete a program of course work that satisfies all course requirements and constraints for a PhD discipline appropriate to the student s research. All students in the PhD program are required to satisfy all PhD requirements as described below. Should the student not be admitted to the PhD program, this program of course work will serve, with the approval of the academic adviser and the ECE Graduate Affairs Committee, to satisfy the course work requirements for the MS. Pass the preliminary (comprehensive) examination at the level required for continuation in the PhD program. A student failing to pass the comprehensive exam at this required level will not be admitted to the PhD program, and will instead continue in the MS program. Maintain a GPA of at least 3.4 in the appropriate core graduate courses. Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: Bullet + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5" A student who has fulfilled all of the above requirements should, after passing the departmental PhD preliminary (comprehensive) exam, submit a petition to change his or her degree objective from MS to PhD. Master of Advanced Studies The Department of Electrical and Computer Engineering offers the master of advanced studies (MAS) degree in Wireless Embedded Systems (WES). The degree requires thirty-six units of work, including a capstone team project. This program is for part-time students with an adequate background in engineering. All the requirements can be completed in two years, with one or two courses taken each quarter. Final Project Capstone Requirement, No Thesis: In the MAS-WES program, an alternative plan requirement is satisfied by a four-unit capstone project requirement. Required Courses: Students entering the MAS program in Electrical and Computer Engineering for a degree in Wireless Embedded Systems will undertake courses in the Wireless Embedded Systems program. The program requires eight four-unit core courses totaling thirty-two units and one four-unit capstone team project course for a total of thirty-six units. All courses must be completed with an average grade of B. The courses required of all students are as follows: WES 267. Digital Signal Processing for Wireless Embedded Systems WES 237A. Introduction to Embedded Systems Design WES 265. Wireless Communications Circuits and Systems WES 237B. Software for Embedded Systems WES 268A. Digital Communications Systems I WES 237C. Hardware for Embedded Systems WES 268B. Digital Communications Systems II WES 269. Codesign of Hardware and Software Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: Bullet + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5"

COPY FROM 2017 18 UC SAN DIEGO GENERAL CATALOG Interim Update (November 1, 2017) Electrical and Computer Engineering ECE (graduate curriculum) WES 207. Capstone Project: Wireless Embedded Systems The Doctoral Programs The ECE department offers graduate programs leading to the PhD in twelve disciplines within electrical and computer engineering, as described in detail below. The PhD is a research degree requiring completion of the PhD program course requirements, satisfactory performance on the comprehensive (PhD preliminary) examination and university qualifying examination, and submission and defense of a doctoral thesis (as described under the Graduate Admission section of this catalog). Students in the PhD program must pass the comprehensive exam (PhD preliminary) before the beginning of the winter quarter of the second year of graduate study. To ensure timely progress in their research, students are strongly encouraged to identify a faculty member willing to supervise their doctoral research by the end of their first year of study. Students should begin defining and preparing for their thesis research as soon as they have passed the comprehensive exam (PhD preliminary). They should plan on taking the university qualifying examination about one year later. The university does not permit students to continue in graduate study for more than four years without passing this examination. At the qualifying examination the student will give an oral presentation on research accomplishments to date and the thesis proposal to a campuswide committee. The committee will decide if the work and proposal has adequate content and reasonable chance for success. They may require that the student modify the proposal and may require a further review. The final PhD requirements are the submission of a dissertation and the dissertation defense (as described under the Graduate Admission section of this catalog). Course Requirements: The total course requirements for the PhD in electrical engineering are essentially the same as the MS, and consist of forty-eight units (twelve quarter courses), of which at least thirty-six units must be in graduate courses. Note that this is greater than the minimum requirements of the university. The department maintains a list of core courses for each disciplinary area from which the thirty-six graduate course units must be selected. The current list may be obtained from the ECE department graduate office or the official website of the department. Students in the interdisciplinary programs may select other core courses with the approval of their academic adviser. The course requirements must be completed within two years of full-time study. Students in the PhD programs may count no more than eight units of ECE 299 toward their course requirements. Students who already hold an MS in electrical engineering must nevertheless satisfy the requirements for the core courses. However, graduate courses taken elsewhere can be substituted for specific courses with the approval of the academic adviser. Degree Requirements: Only upper-division (100-level) and graduate (200-level) courses in which a student is assigned grades A, B, or C (including plus [+] or minus [ ]) can be used to satisfy degree requirements. The fortyeight units of required course work must be taken for a letter grade, except in cases where students take ECE 299, the ECE graduate research course. Graduate research courses in the Jacobs School may be counted toward the degree. Graduate research courses from other departments may be counted toward the degree with curriculum adviser approval. Graduate research courses (i.e., ECE 299) are the only courses where the S/U grading option is allowed. Students must maintain a GPA of 3.0 overall. In addition, a GPA of 3.4 in the core graduate courses is generally expected. Comprehensive Exam (PhD Preliminary): PhD students must find a faculty member who will agree to supervise their thesis research. This should be done before the start of the second year of study. They should then devote at least half their time to research and must pass the PhD preliminary examination before the beginning of the winter

COPY FROM 2017 18 UC SAN DIEGO GENERAL CATALOG Interim Update (November 1, 2017) Electrical and Computer Engineering ECE (graduate curriculum) quarter of the second year of study. This is an oral exam in which the student presents his or her research to a committee of three ECE faculty members, and is examined orally for proficiency in his or her area of specialization. The outcome of the exam is based on the student s research presentation, proficiency demonstrated in the student s area of specialization and overall academic record and performance in the graduate program. Successful completion of the PhD preliminary examination will also satisfy the MS Plan 2 comprehensive exam requirement. University Qualifying Exam: Students who have passed the comprehensive exam (PhD preliminary) should plan to take the university qualifying examination approximately a year after passing the comprehensive exam (PhD preliminary). The university does not permit students to continue in graduate study for more than four years without passing this examination. The university qualifying examination is an oral exam in which the student presents his or her thesis proposal to a university-wide committee. After passing this exam the student is advanced to candidacy. Dissertation Defense: The final PhD requirements are the submission of a dissertation, and the dissertation defense (as described under the Graduate Admission section of this catalog). Students who are advanced to candidacy may register for any ECE course on an S/U basis. Departmental Time Limits: Students who enter the PhD program with an MS from another institution are expected to complete their PhD requirements a year earlier than BS entrants. They must discuss their program with an academic adviser in their first quarter of residence. If their PhD program overlaps significantly with their earlier MS work, the time limits for the comprehensive and qualifying exams will also be reduced by one year. Specific time limits for the PhD program, assuming entry with a BS, are as follows: 1. The Comprehensive Exam (PhD Preliminary) must be completed before the start of the winter quarter of the second year of full-time study. 2. The University Qualifying Exam must be completed before the start of the fifth year of full-time study. 3. Support Limit: Students may not receive financial support through the university for more than seven years of full-time study (six years with an MS). 4. Registered Time Limit: Students may not register as graduate students for more than eight years of fulltime study (seven years with an MS). Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: 1, 2, 3,... + Start at: 1 + Alignment: Left + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5" Half-Time Study: Time limits are extended by one quarter for every two quarters of approved half-time status. Students on half-time status may not take more than six units each quarter. PhD Research Programs 1. Applied Ocean Sciences: This program in applied science related to the oceans is interdepartmental with the Graduate Department of the Scripps Institution of Oceanography (SIO) and the Department of Mechanical and Aerospace Engineering (MAE). It is administered by SIO. All aspects of man s purposeful and unusual intervention into the sea are included. 2. Applied Optics and Photonics: These programs encompass a broad range of interdisciplinary activities involving optical science and engineering, optical and optoelectronic materials and device technology, communications, computer engineering, and photonic systems engineering. Specific topics of interest include ultrafast optical processes, nonlinear optics, quantum cryptography and communications, optical image science, multidimensional optoelectronic I/O devices, spatial light modulators and photodetectors, artificial dielectrics, multifunctional diffractive and micro-optics, volume and computer-generated holography, optoelectronic and micromechanical devices and packaging, modeling and design of photonic devices and systems, photonic integrated circuits and systems, optical sensors, fiber optics, wave modulators and detectors, semiconductor-based optoelectronics, injection lasers, and photodetectors. Current research projects are focused on applications such as optical interconnects in high-speed digital systems, optical multidimensional signal and image processing, ultrahigh-speed optical networks, 3-D Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: 1, 2, 3,... + Start at: 1 + Alignment: Left + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5"

COPY FROM 2017 18 UC SAN DIEGO GENERAL CATALOG Interim Update (November 1, 2017) Electrical and Computer Engineering ECE (graduate curriculum) optical memories and memory interfaces, 3-D imaging and displays, and biophotonic systems. Facilities available for research in these areas include electron-beam and optical lithography, material growth, microfabrication, assembly, and packaging facilities, cw and femtosecond pulse laser systems, detection systems, optical and electro-optic components and devices, and electronic and optical characterization and testing equipment. 3. Communication Theory and Systems: This program in electrical and computer engineering involves the detection of signals, the prediction and filtering of random processes, the design and analysis of communication systems, the analysis of protocols for communication networks, and statistical processing of images. Specific topics include the use of signal processing and error correction coding, and modulation techniques for both data transmission and digital magnetic recording, the use of spread spectrum techniques for wireless communications, and the design and analysis of multiuser communication networks. Additional areas of research include time series analysis, adaptive filtering, sampling design, and wavelet theory. Applications are made to such fields as communications, radar, sonar, oceanography, holography, and image processing. Both theoretical and practical aspects of information processing are studied. 4. Computer Engineering: This program consists of balanced programs of studies in both hardware and software, the premise being that knowledge and skill in both areas are essential both for the modern-day computer engineer to make the proper unbiased tradeoffs in design, and for researchers to consider all paths toward the solution of research questions and problems. Toward these ends, the programs emphasize studies (course work) and competency (comprehensive examinations, and dissertations or projects) in the areas of VLSI and logic design, and reliable computer and communication systems. Specific research areas include computer systems, signal processing systems, multiprocessing and parallel and distributed computing, computer communications and networks, computer architecture, computer-aided design, faulttolerance and reliability, and neurocomputing. 5. Electronic Circuits and Systems: The electronic circuits and systems program involves the study of the processes of analysis and design of electronic circuits and systems. Emphasis is on analog and digital integrated circuits, very large-scale integration (VLSI), analog and digital signal processing, and system algorithms and architectures. Particular areas of study are analog, digital, radio frequency, and microwave electronic circuits and systems, analog-to-digital and digital-to-analog converters, wireless communications transceivers, phase-locked loops, low-power integrated circuits, parallel and multiprocessor computing, electronic neural networks and associative memories, VLSI and algorithmic/application-specific integrated circuit (ASIC) design, microwave and millimeter wave integrated circuits (MIMIC), gallium arsenide ultrahigh-speed integrated circuits and devices (UHSIC), algorithms and architectures for analog and digital signal processing (DSP), high-speed digital communications, computer arithmetic and numerical analysis of finite word length processors, fault-tolerant VLSI systems, design for testability, the design of reliable digital electronic systems, computer-aided design (CAD), and computer-aided engineering (CAE) of DSP/communications systems. 6. Applied Physics Electronic Devices and Materials: The field of electronic devices and materials includes the synthesis, characterization, and application of metals, semiconductors and dielectric materials in solid-state electronic and opto-electronic devices. It encompasses the fabrication, characterization, and modeling of prototype electronic materials, devices, and integrated circuits based on silicon and III-V compound semiconductors as well as processing methods employed in present-day and projected integrated circuits. Current research includes growth by molecular beam epitaxy and chemical vapor phase epitaxy, metallurgical aspects of interfaces, the electronic, optical, and electro-optic properties of heterostructures, and the study of superconductors and magnetic materials. Research thrusts cover the study of ultrasmall structures (nanotechnology) as well as ultrahigh speed transistors and optoelectronic devices. The department has available a complete facility for fabricating prototype silicon and III-V compound transistors and other devices, electron-beam lithography, a Rutherford backscattering facility, molecular beam and organo-metallic vapor-phase epitaxy, cryogenic temperature facilities, scanning tunneling microscopes, microwave and mm-wave measurement facilities, as well as auxiliary apparatus for X-ray, optical, and galvanomagnetic characterization of materials, devices, and components.

COPY FROM 2017 18 UC SAN DIEGO GENERAL CATALOG Interim Update (November 1, 2017) Electrical and Computer Engineering ECE (graduate curriculum) 7. Intelligent Systems, Robotics, and Control: This information sciences-based field is concerned with the design of human-interactive intelligent systems that can sense the world (defined as some specified domain of interest); represent or model the world; detect and identify states and events in the world; reason about and make decisions about the world; and/or act on the world, perhaps all in real-time. A sense of the type of systems and applications encountered in this discipline can be obtained by viewing the projects shown at the ECE department website. Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: 1, 2, 3,... + Start at: 1 + Alignment: Left + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5" The development of such sophisticated systems is necessarily an interdisciplinary activity. To sense and succinctly represent events in the world requires knowledge of signal processing, computer vision, information theory, coding theory, and databasing; to detect and reason about states of the world utilizes concepts from statistical detection theory, hypothesis testing, pattern recognition, time series analysis, and artificial intelligence; to make good decisions about highly complex systems requires knowledge of traditional mathematical optimization theory and contemporary near-optimal approaches such as evolutionary computation; and to act upon the world requires familiarity with concepts of control theory and robotics. Very often learning and adaptation are required as either critical aspects of the world are poorly known at the outset, and must be refined online, or the world is non-stationary and our system must constantly adapt to it as it evolves. In addition to the theoretical information and computer science aspects, many important hardware and software issues must be addressed in order to obtain an effective fusion of a complicated suite of sensors, computers, and problem dynamics into one integrated system. Faculty affiliated with the ISRC subarea are involved in virtually all aspects of the field, including applications to intelligent communications systems; advanced human-computer interfacing; statistical signal- and image-processing; intelligent tracking and guidance systems; biomedical system identification and control; and control of teleoperated and autonomous multiagent robotic systems. 8. Magnetic Recording is an interdisciplinary field involving physics, material science, communications, and mechanical engineering. The physics of magnetic recording involves studying magnetic heads, recording media, and the process of transferring information between the heads and the medium. General areas of investigation include: nonlinear behavior of magnetic heads, very high frequency loss mechanisms in head materials, characterization of recording media by micromagnetic and many body interaction analysis, response of the medium to the application of spatially varying vectorial head fields, fundamental analysis of medium nonuniformities leading to media noise, and experimental studies of the channel transfer function emphasizing nonlinearities, interferences, and noise. Current projects include numerical simulations of high density digital recording in metallic thin films, micromagnetic analysis of magnetic reversal in individual magnetic particles, theory of recorded transition phase noise and magnetization induced nonlinear bit shift in thin metallic films, and analysis of the thermal-temporal stability of interacting fine particles. Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: 1, 2, 3,... + Start at: 1 + Alignment: Left + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5" Research laboratories are housed in the Center for Magnetic Recording Research, a national center devoted to multidisciplinary teaching and research in the field. 9. Applied Physics Space Science: This program focuses on the study of radio waves propagating through turbulent media. The theory of such propagation is also studied with a view to removing the distorting effects of the turbulent medium on astronomical observations and providing an accurate restoration of the intrinsic signals. Space science is concerned with the nature of the sun, its ionized and super-sonic outer atmosphere (the solar wind), and the interaction of the solar wind with various bodies in the solar system. Theoretical studies include the interaction of the solar wind with Earth, planets, and comets; cosmic dusty-plasmas; waves in the ionosphere; and the physics of shocks. A major theoretical effort involves the use of supercomputers for modeling and simulation studies of both fluid and kinetic processes in space plasmas. Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: 1, 2, 3,... + Start at: 1 + Alignment: Left + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5"

COPY FROM 2017 18 UC SAN DIEGO GENERAL CATALOG Interim Update (November 1, 2017) Electrical and Computer Engineering ECE (graduate curriculum) Students are trained in one or more of the interrelated fields, electromagnetics, space plasma physics, radio astronomy, wave propagation, numerical methods, and signal processing. 10. Signal and Image Processing: This program explores engineering issues related to the modeling of signals starting from the physics of the problem, developing and evaluating algorithms for extracting the necessary information from the signal, and the implementation of these algorithms on electronic and opto-electronic systems. Examples of research areas include filter design; fast transforms; adaptive filters; spectrum estimation and modeling; sensor array processing; image processing; motion estimation from images; and the implementation of signal processing algorithms using appropriate technologies with applications in sonar, radar, speech, geophysics, computer-aided tomography, image restoration, robotic vision, and pattern recognition. 11. Nanoscale Devices and Systems: This program area addresses the science and engineering of materials and device structures with characteristic sizes of ~100nm and below, at which phenomena such as quantum confinement and single-electron effects in electronics, near-field behavior in optics and electromagnetics, single-domain effects in magnetics, and a host of other effects in mechanical, fluidic, and biological systems emerge and become dominant. Both fundamental materials, processing, and device technologies, as well as the integration of such technologies into complex systems with consideration of system drivers and constraints as guides for the development of new materials and devices, are encompassed within this program. Specific topics of current interest include the following: nanoscale CMOS technologies; nonsilicon nanoelectronic devices and systems; semiconductor nanowires and other solid-state nanostructures; plasmonic phenomena in nanostructures; nanophotonic materials, devices, and systems; high-density magnetic storage media and systems; nanomagnetic and spintronic devices; micro- and nanofluidics; new technologies for energy generation, conversion, and storage; and advanced sensor devices. Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: 1, 2, 3,... + Start at: 1 + Alignment: Left + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5" Facilities for research in this area include the following: a state-of-the-art shared facility for micro- and nanofabrication (the Qualcomm Institute); electron microscopy and scanned probe microscopy; metal organic chemical vapor deposition and vapor phase epitaxy reactors for thin-film and nanostructure synthesis; molecular-beam epitaxy; high-speed laser systems; comprehensive dc and rf/microwave electrical device characterization systems; optoelectronic and photonic device characterization; and extensive facilities for simulation and computational analysis, including access to facilities at the San Diego Supercomputer Center. 12. Medical Devices and Systems: The program is established for students interested in applying their knowledge in electrical and computer engineering to medicine. The goal of the program is to educate graduate students to improve, develop, and invent electrical engineering hardware/software tools and techniques for the medical field to benefit patients, caregivers, and society. Medicine is one area that has been immensely impacted by electrical and computer engineering. As examples, electrical and computer engineers have played important roles in developing medical imaging scanners, pacemakers, and cochlear implants. One can hardly think of any advanced medical tool for diagnosis, analysis, or treatment that does not involve multiple disciplinary areas of ECE. As point-of-care, genetic, preventive, personalized, and remote medicine become the global trends for twenty-first century, medicine, electrical, and computer engineers will play an increasingly important role in developing safe, effective, and inexpensive health-care solutions. Students choosing to focus their graduate study in this area will be prepared for an industrial or academic career in medical devices and systems. They may find positions in the biotechnology and medical instrument industry, pharmaceutical industry, health-care industry, or electronics and communications industry since many electronic and communication companies are pursuing businesses in medical electronics, telemedicine, cloud computing for medicine, wireless health care, and bioinformatics. Students with advanced degrees may also find academic and research positions in universities, research institutes, government laboratories, and international institutions. Borovoy, Aaron 9/25/2017 1:30 PM Formatted: Outline numbered + Level: 1 + Numbering Style: 1, 2, 3,... + Start at: 1 + Alignment: Left + Aligned at: 0.25" + Tab after: 0.5" + Indent at: 0.5"

COPY FROM 2017 18 UC SAN DIEGO GENERAL CATALOG Interim Update (November 1, 2017) Electrical and Computer Engineering ECE (graduate curriculum) 13. Machine Learning and Data Science: This program prepares students for research, technical, and leadership roles in the rapidly growing field of data science. Spanning the spectrum from fundamental theory to practical applications, students first gain a strong mathematical foundation in probability, statistics, linear algebra, and convex optimization. Building on this groundwork they develop a solid understanding of the algorithmic aspects of statistical-, machine-, and deep-learning, and learn how to implement them in Python, and using scalable and resource-efficient network and processing architectures. These skills are then utilized for hands-on experience in several engineering and scientific application areas including computer vision, robotics, computational biology, neurosciences, image and video processing, social networks, embedded systems, and oceanography. Charmaine Samahi, 12/8/2017 3:21 PM Formatted: No bullets or numbering Research Facilities Most of the research laboratories of the department are associated with individual faculty members or small informal groups of faculty. Larger instruments and facilities, such as those for electron microscopy and e-beam lithography are operated jointly. In addition the department operates several research centers and participates in various university-wide organized research units. The department-operated research centers are the Center for Wireless Communications which is a universityindustry partnership; the Institute for Neural Computation, and the Center for Information Theory and Application in conjunction with the Qualcomm Institute. Department research is also associated with the Center for Astronomy and Space Science, the Center for Magnetic Recording Research, the California Space Institute, the Institute for Nonlinear Science, and the Qualcomm Institute (http://www.calit2.net). Departmental researchers also use various national and international laboratories, such as the National Nanofabrication Facility, the National Radio Astronomy Laboratory, and the Center for Networked Systems (CSE). The department emphasizes computational capability and maintains numerous computer laboratories for instruction and research. One of the NSF national supercomputer centers is located on the campus. This is particularly useful for those whose work requires high data bandwidths.

UNIVERSITY OF CALIFORNIA, SAN DIEGO UCSD BERKELEY DAVIS IRVINE LOS ANGELES MERCED RIVERSIDE SAN DIEGO SAN FRANCISCO SANTA BARBARA SANTA CRUZ DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING, 0404 FAX: (858) 534-7029 9500 GILMAN DRIVE LA JOLLA, CALIFORNIA 92093-0404 January 8, 2018 To: Graduate Council RE: proposal to add the major field of Machine Learning and Data Science to MS and PhD degree programs in ECE This note is to express, on behalf of the CSE department, our support of this new program in ECE. I apologize for the lateness of this note, and it should not be construed as a lack of support. There are two possible concerns about this new program, from our perspective, so I want to acknowledge that we have considered those before voicing our support. The first concern is the overlap with our own program. The CSE department has a long, well-recognized history of excellence in the fields of Artificial Intelligence and Machine Learning. We do not offer major fields as part of our degree program, but obviously we have graduated many PhD and MS students in these fields, and in fact we do have an MS concentration in this area. However, we cannot deny that ECE has also built up strong faculty expertise in the area of Machine Learning, as well. Moreover, the demand placed on our graduate machine learning classes by students outside of our major is quite heavy, and we would welcome the sharing of that load. The second concern is the overlap with graduate programs that will grow out of the Data Science undergraduate program and/or the Halicioglu Data Science Institute. On this matter, I suppose I am also speaking as the interim director of the Data Science undergraduate program. The concern here is that when those programs exist, that it be clear to prospective graduate students where they should apply. Several things come into play. First, I expect this will be a popular course of study for many graduate students, so some sharing of the load will be good. Second, I think there will be sufficient distinction between a Data Science major and a ML and DS field within a major that will help students choose between possible paths. Third, Data Science is by its very nature is inherently multidisciplinary, and I suspect we will end up seeing a number of disciplinespecific data science programs in various departments that isn t exactly what this is, but I do not want to set a precedent for trying to claim sole ownership of the term Data Science ; I think there will be more gained with an inclusive approach to Data Science across campus.

I write these things, not with the intention of introducing objections through the back door, but to assure the council that we have considered these issues, and still choose to support the ECE proposal. I believe it will be an excellent and popular program, backed by a very strong group of faculty already in place. Sincerely, Dean Tullsen Chair, CSE Interim director, Data Science undergraduate program

March 13, 2018 To: UC San Diego Graduate Council Subject: ECE Department Response to Council s Questions Regarding Proposal to Add New Major Field of Study in Machine Learning and Data Science Dear Graduate Council, Thank you very much for your thorough and helpful review of our proposal to establish a new major field of study in Machine Learning and Data Science. Following are our responses to your questions and recommendations. We would first like to clarify the limited scope of our proposal. We are not proposing a new Masters Program in Data Science. Our students will continue to receive their degrees in Electrical and Computer Engineering. We ask only to augment our department s twelve current focus areas, listed on page 4 of our original proposal, with a thirteenth area. Due to the diverse topics covered by the Electrical and Computer Engineering discipline, our graduate students select a focus areas that determines their curriculum. Many of our students are interested in machine learning and data science, and none of the current 12 area curricula fits their needs. Our proposal is to add a 13th focus area, with some additional courses to better suit the needs of our students in this important domain. Your letter also makes two specific requests. First, regarding overlap with offerings in other areas of campus, such as Mathematics and departments in the Social Sciences, we contacted Prof. Lei Ni, chair of the Mathematics Department and met with him along with Professors Peter Ebenfelt (associate dean, division of physical sciences) and Patrick Fitzsimmons (Mathematics vice chair). At the meeting, the Mathematics faculty expressed strong support for our new focus area. Prof. Ni s letter, pledging the Math Department s full support is attached. The Math Department also suggested several of their courses that our students could take. We added these courses (Math 245 ABC, Math 282AB and Math 289C) as optional courses in our proposed curriculum, and will highlight them as recommended Technical Electives. We also went over the graduate courses offered by departments in the Social Sciences. The closest department appears to be Cognitive Science. We met with Cognitive Science Department Chair Marta Kutas who too expressed strong support to our program. In her

attached support letter, Prof. Kutas writes It is very timely and will meet a clear need. I discussed the curriculum with the relevant faculty in my department and they were enthusiastic, especially with regard to applied machine learning courses. The Cognitive Science Department also suggested two of their courses that our students could take, COGS 260 and COGS 289. We added both to the list of recommended Technical Electives in our proposed curriculum. These letters, along with the two original support letters, from CSE department chair Dean Tullsen, and from Jeff Elman and Rajesh Gupta, heads of the new Halicioglu Data Science Institute, show that our program is strongly supported by our most closely related sister departments and the campus data science community. The second important point you mention concerns the required upper-division undergraduate course, ECE 143, in our proposed curriculum. We included this course because Python and data-analysis programming are typically not offered by ECE departments. We thought of this course as an equalizer for incoming students, ensuring that students have a strong foundation in the field. We are open to the possibility of modifying this requirement. For example, we could consider cross-listing the course as both an undergraduate and a graduate course, and running it at two levels to make sure the needs of all our students are met. Please see the attached list of Revised Course Requirements that addresses your concerns. Thank you for your consideration. Prof. Truong Nguyen, Chair Department of Electrical and Computer Engineering Prof. Alon Orlitsky Department of Electrical and Computer Engineering

Machine Learning and Data Science Revised Course Requirements The following 12 courses (48 units) will be required for this major. 4 courses/16 units core courses: ECE 143 - Programming for Data Analysis (instructor varies) ECE 225A - Probability and Statistics for Data Science (Alon Orlitsky) ECE 269 - Linear Algebra (Piya Pal) ECE 271A - Statistical Learning I (Nuno Vasconcelos) 4 additional courses/16 additional units (with at least one course in each area) from: Analytics ECE 271B - Statistical Learning II (Nuno Vasconcelos) ECE 273 - Convex Optimization and Applications (Piya Pal) ECE 275A - Parameter Estimation (Kenneth Kreutz-Delgado) Computation ECE 226 - Optimization and Acceleration of Deep Learning on Various Hardware Platforms (Farinaz Koushanfar) ECE 289, Special Topic - Parallel Processing in Data Science (Siavash Mirarab) ECE 289, Special Topic - Scalable Learning (Bill Lin) Applications ECE 208 - Computational Evolutionary Biology (Siavash Mirarab) ECE 209 (currently ECE 289, Special Topic) - Statistical Learning for Biosignal Processing (Vikash Gilja) ECE 227 - Big Network Data (Massimo Franceschetti) ECE 228 - Machine Learning for Physical Applications (Peter Gerstoft) ECE 268 - Security of Hardware Embedded Systems (Farinaz Koushanfar) ECE 271C - Deep Learning and Applications (Nuno Vasconcelos) ECE 276A - Sensing and Estimation in Robotics (Nikolay Atanasov) ECE 276B - Planning and Learning in Robotics (Nikolay Atanasov) ECE 276C - Advances in Robotics Manipulation (Michael Yip) 4 courses/16 units Technical Electives: Any 4 unit, 200+ course from ECE, CSE, MAE, BENG, CENG, NANO, SE, MATS, MATH, PHYS or COGS taken for a letter grade may be counted. In particular, the following courses are recommended: MATH 245 A-B-C (Convex Analysis and Optimizations), MATH 282 A-B (Applied Statistics), MATH 289C (Exploratory Data Analysis and Inferences), COGS 260 (Image Recognition), and COGS 289 (Machine Learning and Signal Processing for EEG-based Brain Computer Interfaces). Up to 12 units of undergraduate, upper-division ECE coursework may be counted (ECE 111+ only; and not including ECE 195, 197, 198 or 199). MS students (Plan II) are allowed no more than 4 units of ECE 299 (research units) as technical electives. PhD and MS students (Plan I) are allowed no more than 8 units of ECE 299 as technical electives.

COGNITIVE SCIENCE DEPARTMENT -- 0515 (858) 534-6771 9500 GILMAN DRIVE FAX (858) 534-1128 LA JOLLA, CALIFORNIA 92093-0515 February 23, 2018 Dear Truong, Thank you for sharing your department s proposal for a new ECE graduate program in Machine Learning and Data Science. It is very timely and will meet a clear need. I discussed the curriculum with the relevant faculty in my department and they were enthusiastic, especially with regard to applied machine learning courses. As per our discussion, we are pleased to offer our relevant ML courses as a part of the curriculum. Good luck with this thoughtful new program! Marta Kutas, Distinguished Professor and Chair Department of Cognitive Science Director, Center for Research in Language University of California, San Diego

LEI NI, CHAIR DEPARTMENT OF MATHEMATICS, 0112 Phone: (858) 534-1177 9500 GILMAN DRIVE LA JOLLA, CALIFORNIA 92093-0112 Email: leni@math.ucsd.edu RE: Data Science Major in ECE February 27, 2017 Dear Chair Truong, The Department Mathematics is fully supportive to the new program ECE is proposing. There are several graduate courses currently offered by mathematics, which I want to bring your attention to, and can be used by the new program proposed. Math 202 ABC Applied Linear Algebra Math 245 ABC Convex Analysis and Optimizations Math 261 ABC Probabilistic Combinatorics and Algorithms. Math 271 ABC Numerical Optimizations Math 282AB Applied Statistics Math 285 Stochastic Processes Math 287D Statistical Learning Math 289C Exploratory Data Analysis and Inferences. Sincerely, Chair, Department of Mathematics