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 a few of my group's recent advances in machine learning that have the potential to impact health care. The talk will focus in particular on a new approach to learning from temporal data, coupling deep learning with probabilistic inference. Applied to learning disease progression models from clinical data, our algorithms learn a rich representation that is capable of answering counterfactual questions such as which treatment is most appropriate to which patient, providing a new theoretical framework for precision medicine.

Bio:

David Sontag is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, and member of the Institute for Medical Engineering and Science and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Prior to joining MIT, Dr. Sontag was an Assistant Professor in Computer Science and Data Science at New York University from 2011 to 2016, and a postdoctoral researcher at Microsoft Research New England. 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 National Science Foundation Early Career Award. Dr. Sontag received a B.A. from the University of California, Berkeley.