About the Lab

Led by David Sontag, the Clinical Machine Learning Group is interested in advancing machine learning and artificial intelligence, and using these techniques to advance health care.

Broadly, we have two goals:

  • Clinical: To truly make a difference in health care, we need to create algorithms that are useful for solving real clinical problems.
  • Machine learning: We need rigorous solutions, which can pave the way for safe deployment of machine learning in high-stakes settings like healthcare.

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Team

Our team includes postdocs, students, clinical collaborators, and research scientists.

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David Sontag

Associate Professor of EECS

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Irene Chen

PhD Student

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Michael Oberst

PhD Student

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Monica Agrawal

PhD Student

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Zeshan Hussain

MD/PhD Student

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Christina Ji

PhD Student

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Chandler Squires

PhD Student

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Hussein Mozannar

PhD Student

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Hunter Lang

PhD Student

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Jason Zhao

Undergraduate Researcher

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Justin Lim

Master’s student

Recent Publications

Quickly discover relevant content by filtering publications.

Characterization of Overlap in Observational Studies

Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives …

Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models

One of the most surprising and exciting discoveries in supervised learning was the benefit of overparameterization (i.e. training a …

Estimation of Bounds on Potential Outcomes For Decision Making

Estimation of individual treatment effects is often used as the basis for contextual decision making in fields such as healthcare, …

Fast, Structured Clinical Documentation via Contextual Autocomplete

We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical …