- Von Helmholtz Associate Professor of Medical Engineering
- Associate Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
- Principal Investigator, Computer Science and Artificial Intelligence Laboratory
David Sontag joined the MIT faculty in 2017 as Hermann L. F. von Helmholtz Career Development Professor in the Institute for Medical Engineering and Science (IMES) and as Associate Professor in the Department of Electrical Engineering and Computer Science (EECS). He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Sontag’s research interests are in machine learning and artificial intelligence. As part of IMES, he leads a research group that aims to transform healthcare through the use of machine learning.
Prior to joining MIT, Dr. Sontag was an Assistant Professor in Computer Science and Data Science at New York University’s Courant Institute of Mathematical Sciences from 2011 to 2016, and postdoctoral researcher at Microsoft Research New England from 2010 to 2011. Dr. Sontag received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award. Dr. Sontag received a B.A. from the University of California, Berkeley.
- Ph.D. in Electrical Engineering and Computer Science, Massachusetts
Institute of Technology, 2010
- S.M. in Electrical Engineering and Computer Science, Massachusetts
Institute of Technology, 2007
- B.A. in Computer Science, University of California, Berkeley, 2005
Professor Sontag’s research both aims to advance the field of machine learning and artificial intelligence, and to apply these to transform healthcare.
These are exciting times for the practice of medicine. The rapid adoption of electronic health records and has created a wealth of new data about patients, which is a goldmine for improving our understanding of human health. Our lab develops algorithms that use this data to better understand disease progression and to facilitate new, precise treatment strategies for a wide range of diseases and conditions such as Type 2 diabetes, which affects tens of millions of people worldwide every year, and multiple myeloma, a rare blood cancer. In pursuit of these aims, a major methodological focus has been on developing novel approaches to modeling high-dimensional time-series data, particularly approaches that bring together probabilistic modeling and deep learning, and causal inference from observational data.
Intelligent Electronic Health Records
Today’s electronic health records are predominately a place for recording a patient’s health data. We aim to develop the foundation for the next-generation of intelligent electronic health records, where machine learning and artificial intelligence is built-in to help with medical diagnosis, automatically trigger clinical decision support, personalize treatment suggestions, autonomously retrieve relevant past medical history, make documentation faster and higher quality, and predict adverse events before they happen. A major challenge is the need for robust machine learning algorithms that are safe, interpretable, can learn from little labeled training data, understand natural language, and generalize well across medical settings and institutions.
- R. Krishnan, U. Shalit, D. Sontag. “Structured Inference Networks for Nonlinear State Space Models.” To appear in the Thirty-First AAAI Conference on Artificial Intelligence. (2017).
- F. Johansson, U. Shalit, D. Sontag. “Learning Representations for Counterfactual Inference.” 33rd International Conference on Machine Learning (ICML). (2016).
- 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) 23.4 (2016): 731-40.
- S. Blecker, S. D. Katz, L. Horwitz, G. Kuperman, H. Park, A. Gold, D. Sontag. “Comparison of Approaches for Heart Failure Case Identification From Electronic Health Record Data.” Journal of the American Medical Association (JAMA) Cardiology 1.9 (2016): 1014-20.
- Y. Kim, Y. Jernite, D. Sontag, S. Rush. “Character-Aware Neural Language
Models.” Thirtieth AAAI Conference on Artificial Intelligence. (2016).
- N. Razavian, S. Blecker, A. M. Schmidt, A. Smith-McLallen, S. Nigam, D.
Sontag. “Population-Level Prediction of Type 2 Diabetes using Claims Data and Analysis of Risk Factors.” Big Data 3.4 (2015): 277-87.
- X. Wang, D. Sontag, F. Wang. Unsupervised Learning of Disease Progression Models. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Aug. 2014.
- Y. Jernite, Y. Halpern, D. Sontag. “Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests.” Neural Information Processing Systems (NIPS) Proceedings of the 26th International Conference (2013): 2355-63.
- E. Brenner, D. Sontag. “SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure.” Uncertainty in Artificial Intelligence (UAI) Proceedings of the 29th Conference. (2013): 112-121.
- 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) 28 (2013): 280-88.
- Guidelines for reinforcement learning in healthcare. Gottesman, O; Johansson, F; Komorowski, M; Faisal, A; Sontag, D; Doshi-Velez, F; and Celi, L. Nature medicine, 25(1): 16–18. 2019. https://finale.seas.harvard.edu/files/finale/files/guidelines_for_reinforcement_learning_in_healthcare.pdf
A full list of Professor Sontag’s publications can be found on his website.