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David Sontag

Associate Professor of Electrical Engineering and Computer Science at MIT

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).…

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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.

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How is Machine Learning Going to Change Health Care?
Abstract: Machine learning has transformed the technology industry over the last decade, forming the basis for web search, speech recognition, product recommendations, and self-driving cars. With the increased adoption of electronic health records and a surge in funding for health IT startups, health care is undergoing a similar transformation. I will talk about how machine learning has the potential to change health care across the spectrum, from enabling the next-generation electronic health record to population-level risk stratification from health insurance claims (examples will come from my group’s work). These innovations will lead to increased quality of care and decreased cost.

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Machine Learning in Medicine - Challenges to Bringing Artificial Intelligence to Health Care

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