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

Professor of EECS

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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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Shannon Shen

PhD Student

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Ilker Demirel

PhD Student

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Elizabeth Bondi-Kelly

Postdoctoral Fellow

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Alejandro Buendia

Research Engineer

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Jimin Lee

Undergraduate Researcher

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Niklas Mannhardt

Master’s student

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Penny Brant

Undergraduate Researcher

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Sama Setty

Undergraduate Researcher

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Sharon Jiang

Master’s student

Recent Publications

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Conformalized Unconditional Quantile Regression

We develop a predictive inference procedure that combines conformal prediction (CP) with unconditional quantile regression (QR)—a …

Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions

Randomized Controlled Trials (RCT)s are relied upon to assess new treatments, but suffer from limited power to guide personalized …

TabLLM: Few-shot Classification of Tabular Data with Large Language Models

We study the application of large language models to zero-shot and few-shot classification of tabular data. We prompt the large …

Who Should Predict? Exact Algorithms For Learning to Defer to Humans

Algorithmic predictors should be able to defer the prediction to a human decision maker to ensure accurate predictions. In this work, …