David Sontag's Home Page

I am an Associate Professor of Electrical Engineering and Computer Science at MIT, part of both the Institute for Medical Engineering & Science and the Computer Science and Artificial Intelligence Laboratory. My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care. Previously, I was an Assistant Professor of Computer Science and Data Science at New York University, part of the CILVR lab.

News

• Released our Python (Theano) code for learning Deep Markov Models!
• Video of our ICML 2016 tutorial on causal inference for observational studies (slides)

Teaching

Publications

All publications sorted by year
All publications sorted by topic (ML/AI, Health)

Selected papers:

• U. Shalit, F. Johansson, D. Sontag. Estimating Individual Treatment Effect: Generalization Bounds and Algorithms. 34th International Conference on Machine Learning (ICML), 2017. [code] [Slides]
• M. Rotmensch, Y. Halpern, A. Tlimat, S. Horng, D. Sontag. Learning a Health Knowledge Graph from Electronic Medical Records, Nature Scientific Reports, July 2017. Supplementary
• R. Krishnan, U. Shalit, D. Sontag. Structured Inference Networks for Nonlinear State Space Models, Thirty-First AAAI Conference on Artificial Intelligence, Feb. 2017. [code] Older version
• Y. Halpern, S. Horng, Y. Choi, D. Sontag. Electronic Medical Record Phenotyping using the Anchor and Learn Framework. Journal of the American Medical Informatics Association (JAMIA), April 2016. [html] [code] [Slides]
• Y. Kim, Y. Jernite, D. Sontag, S. Rush. Character-Aware Neural Language Models, Thirtieth AAAI Conference on Artificial Intelligence, Feb. 2016. [code] [Slides] Video
• A. Globerson, T. Roughgarden, D. Sontag, C. Yildirim. How Hard is Inference for Structured Prediction? 32nd International Conference on Machine Learning (ICML), July 2015. arXiv [Slides]
• X. Wang, D. Sontag, F. Wang. Unsupervised Learning of Disease Progression Models. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Aug. 2014. [Slides] BibTex
• Y. Jernite, Y. Halpern, D. Sontag. Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests. Neural Information Processing Systems (NIPS) 26, Dec. 2013. Supplementary [Code] BibTex
• E. Brenner, D. Sontag. SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. Uncertainty in Artificial Intelligence (UAI) 29, July 2013. BibTex [arXiv]
• S. Arora, R. Ge, Y. Halpern, D. Mimno, A. Moitra, D. Sontag, Y. Wu, M. Zhu. A Practical Algorithm for Topic Modeling with Provable Guarantees. 30th International Conference on Machine Learning (ICML), 2013. Supplementary Video BibTex
• T. Koo, A. Rush, M. Collins, T. Jaakkola, and D. Sontag. Dual Decomposition for Parsing with Non-Projective Head Automata. Empirical Methods in Natural Language Processing (EMNLP), 2010. Best paper award. BibTex
• T. Jaakkola, D. Sontag, A. Globerson, M. Meila. Learning Bayesian Network Structure using LP Relaxations. 13th International Conference on Artificial Intelligence and Statistics (AI-STATS), 2010. BibTex
• D. Sontag. Approximate Inference in Graphical Models using LP Relaxations. Ph.D. thesis, Massachusetts Institute of Technology, 2010. George M. Sprowls Award for the best doctoral theses in Computer Science at MIT (2010). BibTex
• D. Sontag, T. Meltzer, A. Globerson, Y. Weiss, T. Jaakkola. Tightening LP Relaxations for MAP using Message Passing. Uncertainty in Artificial Intelligence (UAI) 24, July 2008. Best paper award. [code] BibTex
• D. Sontag, T. Jaakkola. New Outer Bounds on the Marginal Polytope. Neural Information Processing Systems (NIPS) 20, Dec. 2007. Outstanding student paper award. Addendum BibTex
• B. Milch, B. Marthi, S. Russell, D. Sontag, D. L. Ong, and A. Kolobov. BLOG: Probabilistic Models with Unknown Objects. In Lise Getoor and Ben Taskar, eds. Statistical Relational Learning. Cambridge, MA: MIT Press, 2007.

Code