Overview of the NSF Programs

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1 Overview of the NSF Programs NSF Workshop on Real Time Data Analytics for the Resilient Electric Grid August 4 5, 2018 Portland, OR EPCN Program Directors Anil Pahwa Any opinion, finding, conclusion, or recommendation expressed in this Anthony Kuh material are those of the author and do not necessarily reflect the views of Kishan Baheti the National Science Foundation.

2 Outline Introduction Energy, Power, Control and Networks (EPCN) Core Program Multi-directorate Programs Cyber-Physical Systems Smart and Connected Communities Digital Grid NSF 10 Big Ideas Harnessing the Data Revolution Workshop Outcomes and Expectations

3

4 : Franklin, Bush, and Truman

5 1950: The Founding Act On 10 May 1950, President Truman signed Public Law [PUBLIC LAW ST CONGRESS] [CHAPTER 171-2D SESSION] [S. 247] AN ACT To promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense; and for other purposes. Be it enacted by the Senate and House of Representatives of the United States of America in Congress assembled, That this Act may be cited as the "National Science Foundation Act of 1950". ESTABLISHMENT OF NATIONAL SCIENCE FOUNDATION SEC. 2. There is hereby established in the executive branch of the Government an independent agency to be known as the National Science Foundation (hereinafter referred to as the Foundation ). The Foundation shall consist of a National Science Board (hereinafter referred to as the "Board") and a Director.

6 Unique Features of NSF Supports fundamental research and education across all fields of science and engineering Discipline-based structure with cross-disciplinary mechanisms Emphasis on integrating research and education Close interaction with Universities Rotator System: About 50% Program Directors are on loan from universities, labs, or industries FY2018 NSF Appropriation of $7.77 billion

7 EPCN Program Role and Priorities Foundational research in systems and control: modeling, analysis, design, optimization, learning, adaptation, and control Massive data, real-time decisions Networked and controlled at multiand extreme scales Dynamically reorganizing/reconfiguring Dependable operation with high assurance reliability, safety, cybersecurity, and usability

8 EPCN Program Focus Control and Networked Systems Systems with both discrete and continuous dynamics Distributed Scalable, decisions and actions with local information faster, distributed computation algorithms Cyber-Physical Learning Data security of control systems and Adaptive Systems analytics and Intelligent systems Machine Neural learning algorithms (deep learning) networks architectures and applications

9 EPCN Program Focus Energy and Power Systems Integration of solar, wind, storage with grid Monitoring, protection, and resilient operation Cyber security, market design, consumer behavior Microgrid, Advanced Energy efficient buildings Power Electronics Energy efficient power electronics converters Electric and hybrid vehicles Storage devices, Electric Machines Energy Harvesting 9

10 Cyber-Physical Systems (CPS) Application Sectors Abstract from application sectors to more foundational principles Apply these principles to problems in new sectors Safe, secure, reliable, verification, real-time adaptation. energy aeronautics automotive agricultur e Safety Control Verification Core Science, Technology, Engineering Real-time Networking Systems Security medical chemical material s 10 manufacturi ng civil

11 Smart and Connected Communities Program $18.5M cross-directorate FY 2017 investment Fundamen tal Science Community and Impact Engineeri ng Fundamental science and engineering research questions that must be addressed for community impact? Universities, in partnerships with community stakeholders, have a key role in enabling Smart and Connected Communities of the future 11

12 Next Generation Power Systems Digital Grid Extensive deployment of sensing, communications and information technologies The rise of distributed generation (DG) and microgrids Increasing penetration of wind and solar Changing demand profiles and consumer expectations Cybersecurity Need of power grid for resilient grid operation in the face of natural and man-made

13 NSF 10 Big Ideas RESEARCH IDEAS Windows on the Universe: The Era of Multimessenger Astrophysics Work at the HumanTechnology Frontier: Shaping the Future Harnessing Data for 21st Century Science and Engineering The Quantum Leap: Leading the Next Quantum Revolution Understanding the Rules of Life: Predicting Phenotype Navigating the New Arctic PROCESS IDEAS Mid-scale Research Infrastructure NSF 2050: Seeding Innovation Growing Convergent Research at NSF NSF-INCLUDES: Enhancing Science and Engineering through Diversity 2 13

14 NSF Big Data Initiatives Big NSF (led by CISE) Research Critical Techniques and Methodologies for Advancing Foundations of Big Data Sciences and Engineering (BIGDATA) Computational and Data-Enabled Science and Engineering (CDSE) Cyber Infrastructure Education and Workforce Community Building Big Data Regional Innovation Hubs and Spokes Events 14

15 BIO CISE EHR ENG GEO MPS SBE OIA OISE Directorates, Offices Big Ideas 15

16 Harnessing the Data Revolution: Five themes Research across all NSF Directorates Dataintensive research in all areas of science and engineering Theoretical foundation s mathematics, statistics, computer & computational science Systems, algorithm s data-centric algorithms, systems Science domains Advanced cyberinfrastruct ure Foundations Systems, Algorithms Cyber infrastructure Accelerating data-intensive research Educational pathways Education, Workforce Innovations grounded in an education-research-based framework 4 16

17 HDR Institutes to Tackle Data Challenges in DataIntensive Science and Engineering research DataIntensive Science & Engineering E.g., Real-time data: Sensing, analysis, assimilation, decision-making with real-time data streams E.g., Integration across multi-scale, multi-modal data, with data assimilation, to enable forecasting, predictions, All HDR activities are linked. Institutes must link with: Foundations: TRIPODS Centers Systems/Algorithms: Open Knowledge Network, Model Commons Education and Workforce Development: HDR Academy, Data Science Corps 7 17

18 HDR Foundations Foundations NSF Transdisciplinary Research in Principles of Data Science (TRIPODS) Program Establish collaborations among computer and computational scientists, statisticians, and mathematicians Develop the principles of data science as distinct from CS, statistics, and mathematics 5 18

19 HDR Systems and Algorithms Systems, Algorithms Open Knowledge Network An open web-scale knowledge network of semanticallylinked concepts and data Would foster research on an entire class of new applications leveraging data, context, and inferences from data Should support question/answer interfaces, dialog-based interactions, explanatory/story-telling interfaces Model Commons Enabling sharing and resuse of data-intensive models including machine learning models Provides support for reproducibility, transfer learning, 6 19

20 Education and Workforce Development: HDR Academy Catalog, collect, create education/training materials Collect data science education / training materials from HDR projects Other related NSF projects Other, non-nsf sources HDR Postdocs, undergrads Education, Workforce Place postdocs and REU students in cross-training positions e.g., domain scientist placed with a data group, and vice versa HDR Bootcamps Offer data science bootcamps for grad students; postdocs & junior faculty; senior faculty 8 20

21 Real-Time Sensing, Learning, and Decision Making for Engineering Systems Engineering systems described by complex physical models, but increasingly getting real-time sensing data (heterogeneous, different spatial, and temporal domains) How to combine dynamic physical models with data analytic approaches to make real-time decisions Combine different disciplines: domain application expertise, control, optimization, statistic, signal processing, and machine learning 21

22 Engineering Efforts in RTL-DM Real-Time Learning and Decision Making in Dynamical Engineering Systems Workshop: Le Xie (brought different communities together) Related workshops: ACC, PESGM Real-D: Dear Colleague Letter: Real-Time Learning and Decision Making in Engineered Systems (EAGER funding opportunities in Engineering) HDR (DISE institutes: Real-Time Sensing, Learning, and Decision Making for Science and Engineering Applications) 22

23 Thank You!!! Contact Information: 23

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