Machine Learning / Duolingo Seminar

  • Remote Access - Zoom
  • Virtual Presentation - ET
  • Associate Professor
  • Department of Electrical Engineering and Computer Science
  • Massachusetts Institute of Technology

Learning Deep Markov Models for Precision Medicine

I present 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 rich representations that are capable of answering counterfactual questions such as which treatment is most appropriate to which patient, providing a new theoretical framework for precision medicine.

Making valid causal inferences from observational data requires a number of assumptions to be satisfied. I show how machine learning can be used to test and explain one of these (overlap) and how machine learning can help circumvent another (hidden confounding). Along the way, I'll make connections to recent work on domain adaptation and dataset shift.

Finally, I discuss my vision for the future, where these methods are scalably used to guide millions of patients' health care. Doing so will require policy and legislative changes to improve health data collection and curation, new algorithms for extracting treatment and outcomes from clinical text, and advances in human-computer interaction to safely and effectively explain algorithm predictions to patients and providers.

David Sontag is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, and member of the Institute for Medical Engineering and Science (IMES) 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 (NeurIPS), 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.

Zoom Participation. See announcement.

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