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

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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 X 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

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Aayush Gupta

Undergrad Student

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Sol Rodriguez

Undergraduate Researcher

Recent Publications

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Graph cuts always find a global optimum for Potts models (with a catch)

We prove that the alpha-expansion algorithm for MAP inference always returns a globally optimal assignment for Markov Random Fields …

Neural Pharmacodynamic State Space Modeling

Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression. However, …

Regularizing towards Causal Invariance: Linear Models with Proxies

We propose a method for learning linear models whose predictive performance is robust to causal interventions on unobserved variables, …