Curriculum Vitae - Jin Tian

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1 Curriculum Vitae - Jin Tian Department of Computer Science 226 Atanasoff Hall Iowa State University Ames, IA Phone: (515) Fax: (515) jtian@iastate.edu Web: Research Interests Artificial Intelligence and Machine Learning: probabilistic graphical models, causal inference. Education Ph.D. Computer Science, University of California, Los Angeles 2002 Advisor: Judea Pearl Dissertation: Studies in Causal Reasoning and Learning M.S. Physics, University of California, Los Angeles 1997 B.S. Physics, Tsinghua University, P. R. China 1992 Employment Aug present Aug July 2010 Fall 1997 Spring 2002 Associate Professor, Department of Computer Science, Iowa State University Assistant Professor, Department of Computer Science, Iowa State University Graduate Student Researcher, Computer Science department, University of California, Los Angeles. Teaching Experience Fall Spring 2003, 2005, 2007, 2014, 2016 Fall 2015 Spring 2015 Spring , Fall 2010 Fall Spring 2004, 2010 Spring 2006, 2007 Fall 2003, 2004, 2006 Fall 1998 Summer 1997 ComS 472/572 Principles of Artificial Intelligence, ISU ComS 673 Advanced Topics in Computational Intelligence, ISU ComS 107, 107XW Applied Computer Programming, ISU ComS 474 Introduction to Machine Learning ComS 573 Machine Learning, ISU ComS 572 Principles of Artificial Intelligence, ISU ComS 472 Principles of Artificial Intelligence, ISU ComS 672 Advanced Topics in Computational Models of Learning,ISU Com S 363: Introduction to Database Systems, ISU ComS 474 Elements of Neural Computation, ISU Teaching Assistant, Computer Science department, UCLA. CS 33 Systems Programming Assembly language Teaching Assistant, Physics and Astronomy department, UCLA. Physics 6C Lab 1

2 Spring 1997 Teaching Assistant, Physics and Astronomy department, UCLA. Physics 6A Lab Research Grants RI: Medium: Collaborative Research: Causal Inference: Identification, Learning, and Decision-Making. PI: Jin Tian, NSF, $260,169, Aug 1, Jul 31, Collaborative with Elias Bareinboim (Purdue), Judea Pearl (UCLA). Predictive and Causal Modeling with Causal Graphical Models, PI: Jin Tian. Office of Naval Research (ONR), $380,749, 01/01/ /31/2020. Bridging the Digital Divide in Data Science: Invention and Refinement of Shared Data Science Infrastructures, PI: Hridesh Rajan, 12 Co-PIs. Iowa State University Presidential Initiative for Interdisciplinary Research in Data Driven Science. $450K, CAREER: Reasoning with Cause and Effect: Model Testing, Axiomatization, and Identification. PI: Jin Tian. National Science Foundation, $455,452. Refereed Publications Yanpeng Zhao, Yetian Chen, Kewei Tu, and Jin Tian. Learning Bayesian Network Structures Under Incremental Construction Curricula. Neurocomputing. Volume 258, Pages 30-40, Ru He, Jin Tian, and Huaiqing Wu, Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling. Journal of Machine Learning Research (JMLR) 17(101):1-54, Yetian Chen, José P. González-Brenes, and Jin Tian. Joint Discovery of Skill Prerequisite Graphs and Student Models, in Proceedings of the 9th International Conference on Educational Data Mining (EDM), Raleigh, North Carolina, USA. (Full Paper) Yanpeng Zhao, Yetian Chen, Kewei Tu, and Jin Tian. Curriculum Learning of Bayesian Network Structures, in Proceedings of the 7th Asian Conference on Machine Learning (ACML), Hong Kong, J. Tian. Missing at Random in Graphical Models, in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 2015, San Diego, CA, USA. JMLR: W&CP volume 38. Y. Chen, L. Meng, and J. Tian. Exact Bayesian Learning of Ancestor relations in Bayesian Networks, in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 2015, San Diego, CA, USA. JMLR: W&CP volume 38. E. Bareinboim and J. Tian, Recovering Causal Effects from Selection Bias, in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), January 25 30, Austin, Texas, USA. 2

3 Y. Chen and J. Tian, Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), July 27-31, Quebec City, Canada. B. Chen, J. Tian, and J. Pearl, Testable Implications of Linear Structural Equations Models, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), July 27-31, Quebec City, Canada. E. Bareinboim, J. Tian, and J. Pearl, Recovering from Selection Bias in Causal and Statistical Inference, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), July 27-31, Quebec City, Canada. (AAAI-14 Best Paper Award) K. Mohan, J. Pearl, and J. Tian, Graphical Models for Inference with Missing Data, in C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 26 (NIPS 2013), , R. He, J. Wang, J. Tian, C. Chu, B. Mauney, and I. Perisic, Session Analysis of People Search within a Professional Social Network, Journal of the American Society for Information Science and Technology, 64: , doi: /asi J. Tian, and I. Shpitser, On Identifying Causal Effects, In R. Dechter, H. Geffner, and J. Halpern (Eds.), Heuristics, Probability and Causality: A Tribute to Judea Pearl, College Publications, J. Tian, R. He, and L. Ram, Bayesian Model Averaging Using the k-best Bayesian Network Structures, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Island, California. J. Tian, Parameter Identification in a Class of Linear Structural Equation Models, in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), July 11-17, Pasadena, California. pp J. Tian and R. He, Computing Posterior Probabilities of Structural Features in Bayesian Networks, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), June 18-21, Montreal, Canada. C. Kang and J. Tian, Markov Properties for Linear Causal Models with Correlated Errors, Journal of Machine Learning Research (JMLR), Vol. 10, 41-70, J. Tian, Identifying Dynamic Sequential Plans, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 9-12, Helsinki, Finland. AUAI Press, pp Z. Cai, M. Kuroki, J. Pearl, and J Tian, Bounds on Direct Effects in the Presence of Confounded Intermediate Variables, Biometrics, Vol. 64, , J. Tian, On the Identification of a Class of Linear Models. Proceedings of the National Conference on Artificial Intelligence (AAAI), July 22-26, Vancouver, Canada. AAAI Press, pp

4 J. Tian, A Criterion for Parameter Identification in Structural Equation Models, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 19-22, Vancouver, Canada. AUAI Press, pp C. Kang, J. Tian, Polynomial Constraints in Causal Bayesian Networks, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 19-22, Vancouver, Canada. AUAI Press, pp J. Tian, C. Kang, and J. Pearl, A Characterization of Interventional Distributions in Semi- Markovian Causal Models. Proceedings of the National Conference on Artificial Intelligence (AAAI), July 16-20, Boston, Massachusetts. AAAI Press, pp C. Kang and J. Tian, Inequality Constraints in Causal Models with Hidden Variables, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 13-16, Cambridge, Massachusetts. AUAI Press, pp C. Kang and J. Tian, A Hybrid Generative/Discriminative Bayesian Classifier. Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference (FLAIRS), May 11-13, Melbourne Beach, Florida. AAAI Press, pp C. Kang and J. Tian, Local Markov Property for Models Satisfying Composition Axiom. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 26-29, Edinburgh, Scotland. AUAI Press, pp J. Tian, Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 26-29, Edinburgh, Scotland. AUAI Press, pp J. Tian, Identifying Direct Causal Effects in Linear Models. Proceedings of the National Conference on Artificial Intelligence (AAAI), July 9-13, Pittsburgh, Pennsylvania. AAAI Press, pp J. Tian, Identifying Conditional Causal Effects. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 9-11, Banff, Canada. AUAI Press. pp J. Tian, Identifying Linear Causal Effects. Proceedings of the National Conference on Artificial Intelligence (AAAI), July 25-29, San Jose, California. AAAI Press. pp J. Tian and J. Pearl, On the Testable Implications of Causal Models with Hidden Variables, in Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), August 1-4, 2002, Edmonton, Alberta, Canada. Morgan Kaufmann, 2002, J. Tian and J. Pearl, A general identification condition for causal effects, in Proceedings of the National Conference on Artificial Intelligence (AAAI), July 28 - August 1, 2002, Edmonton, Alberta, Canada. AAAI Press, 2002,

5 J. Tian and J. Pearl, A new characterization of the experimental implications of causal Bayesian networks, in Proceedings of the National Conference on Artificial Intelligence (AAAI), July 28 - August 1, 2002, Edmonton, Alberta, Canada. AAAI Press, 2002, J. Tian and J. Pearl, Causal Discovery from Changes, in Jack S. Breese, Daphne Koller (Eds.), Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence (UAI), University of Washington, Seattle, Washington, USA, August 2-5, Morgan Kaufmann, 2001, J. Tian and J. Pearl, Probabilities of causation: bounds and identification, Annals of Mathematics and Artificial Intelligence, Vol. 28, , J. Tian and J. Pearl, Probabilities of causation: bounds and identification, in Craig Boutilier, Moisés Goldszmidt (Eds.), Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence (UAI), Stanford University, Stanford, California, USA, June 30 - July 3, Morgan Kaufmann 2000, J. Tian, A branch-and-bound algorithm for MDL learning Bayesian networks, in Craig Boutilier, Moisés Goldszmidt (Eds.), Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence (UAI), Stanford University, Stanford, California, USA, June 30 - July 3, Morgan Kaufmann 2000, Technical Reports Jin Tian. Recoverability of Joint Distribution from Missing Data. arxiv: , Yetian Chen, Jin Tian, Olga Nikolova, Srinivas Aluru. A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks. arxiv: , J. Tian and J. Pearl, Causal Discovery from Changes: a Bayesian Approach, Cognitive Systems Laboratory Technical Report R-285, Computer Science department, UCLA, February J. Tian, A. Paz, and J. Pearl, Finding Minimal D-separators, Cognitive Systems Laboratory Technical Report R-254, Computer Science department, UCLA, Invited Talks Inference with Selection Bias in Causal Graphical Models, Department of Statistics, Iowa State University, January Bayesian Approach to Causal Structure Learning, Workshop on Statistical Causal Inference and its Applications to Genetics, Montreal, Canada, July 2016 Recovering from Selection Bias in Causal and Statistical Inference, Purdue University, Recovering from Selection Bias in Causal and Statistical Inference, Kyoto International Conference on Modern Statistics, Kyoto, Japan, November

6 Recovering from Selection Bias in Causal and Statistical Inference, International Workshop on Causal Inference and its Related Topics, Tokyo, Japan, November Causal Inference and Algebraic Methods, SAMSI Workshop on Algebraic Statistical Models, 2009 Causal inference with graphical models, WNAR/IMS annual meeting Tutorial: Causal inference and graphical models. Conference on Uncertainty in Artificial Intelligence (UAI) Scholarships & Honors Faculty Early Career Development Award, National Science Foundation 2004 Microsoft Graduate Student Fellowship Department of Physics and Astronomy Fellowship, UCLA Professional Service Associate Editor, Artificial Intelligence Journal, since Editorial board, Journal of Causal Inference, since General Chair, the Conference on Uncertainty in Artificial Intelligence (UAI), Program Co-Chair, the Conference on Uncertainty in Artificial Intelligence (UAI), Associate Editor, Electronic Journal of Statistics, Proposal review panel, National Science Foundation (NSF), 2005, 2008, 2011, 2012, Program committee member, the International Joint Conference on Artificial Intelligence (IJCAI) Senior program committee member, 2011, 2013, 2016 Program committee member, European Conference on Artificial Intelligence (ECAI- 2016). Program committee member, the Conference on Uncertainty in Artificial Intelligence (UAI) , Senior program committee member, , , 2017 Program committee member, the National Conference on Artificial Intelligence (AAAI), 2002, 2006, Program committee member, the Midwest Artificial Intelligence and Cognitive Science Conference (MAICS) , 2009, Reviewer, the International Conference on Machine Learning (ICML) Program committee member, SIAM International Conference on Data Mining (SDM) Program committee member, the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Referee 6

7 Journal of Machine Learning Research (JMLR), 2005, , 2012, 2013, 2016, 2017 Journal of the American Statistical Association (JASA), 2010, Neurocomputing, 2016 Computational Statistics and Data Analysis, 2015 ACM Computing Surveys, 2015 Journal of Statistical Software (JSS), 2015 Journal of Parallel and Distributed Computing, 2013 Journal of Artificial Intelligence, 2004, 2007, 2009, 2010 Journal of Artificial Intelligence Research, 2003, 2004, 2010 Annals of Statistics, 2007, 2010, 2012 Journal of Applied Statistics, 2011 IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011 Frontier of Mathematics in China, IEEE Machine Learning for Signal Processing Workshop, 2012 The Review of Economics and Statistics, 2010 Information Technology and Management, 2009 Acta Mathematica Sinica, 2009 Annals of Mathematics and Artificial Intelligence, 2003, 2008 Machine Learning Journal, 2008 IEEE Transactions on Information Theory, 2008 NIPS Workshop on Causality, 2008 IMA volume on Emerging Applications of Algebraic Geometry, 2007 The Economic Journal, 2007 International Joint Conference on Artificial Intelligence (IJCAI), 2005, 2007 Biometrika, 2006 Management Science, 2006 Risk Analysis, 2005 Journal of Multivariate Analysis, 2005 Book review for Cambridge University Press, 2005 The International Conference on Knowledge Based Computer Systems (KBCS), 2004 Journal of the Royal Statistics Society Series B,

University of Massachusetts Amherst Department of Civil and Environmental Engineering. Newton, MA Transportation Engineer Nov Aug 2007

University of Massachusetts Amherst Department of Civil and Environmental Engineering. Newton, MA Transportation Engineer Nov Aug 2007 Song Gao 214C Marston Hall 130 Natural Resources Road Amherst, MA 01003-0724 Tel: (413) 545-2688 Fax: (413) 545-9569 E-mail: songgao@ecs.umass.edu Education Massachusetts Institute of Technology Cambridge,