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.

News

Team

Avatar

David Sontag

Associate Professor of EECS

Avatar

Irene Chen

PhD Student

Avatar

Michael Oberst

PhD Student

Avatar

Monica Agrawal

PhD Student

Avatar

Zeshan Hussain

MD/PhD Student

Avatar

Christina X Ji

PhD Student

Avatar

Chandler Squires

PhD Student

Avatar

Hussein Mozannar

PhD Student

Avatar

Hunter Lang

PhD Student

Avatar

Yuria Utsumi

Master’s student

Avatar

Ming-Chieh Shih

Postdoctoral Fellow

Avatar

Sharon Jiang

Undergraduate Researcher

Avatar

Sol Rodriguez

Undergraduate Researcher

Recent Publications

Quickly discover relevant content by filtering publications.

Beyond perturbation stability: LP recovery guarantees for MAP inference on noisy stable instances

Several works have shown that perturbation stable instances of the MAP inference problem in Potts models can be solved exactly using a …

PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming

Data cleaning is naturally framed as probabilistic inference in a generative model of ground-truth data and likely errors, but the …