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 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

• August 2024 “Towards Verifiable Text Generation with Symbolic References” is accepted at COLM 2024
• August 2024 Shannon presented “Learning to Decode Collaboratively with Multiple Language Models” at ACL 2024
• July 2024 Ilker presented “Prediction-powered Generalization of Causal Inferences” at ICML 2024
• July 2024 “Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma” was published in Nature Digital Medicine
• June 2024 Stefan presented “A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models” at CHIL 2024
• May 2024 Ilker presented “Benchmarking Observational Studies with Experimental Data under Right-Censoring” at AISTATS 2024
• December 2023 Rebecca presented “A Deep Dive into Single-Cell RNA Sequencing Foundation Models” at MLCB 2023
• August 2023 Sharon presented “Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes” at MLHC 2023
• April 2023: We have three papers at AISTATS 2023, with
  • Hussein presenting “Who Should Predict? Exact Algorithms For Learning to Defer to Humans”
  • Zeshan presenting “Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions”
  • Zeshan and Ahmed presenting “Conformalized Unconditional Quantile Regression”
• December 2022 Monica presented “Large Language Models are Few-Shot Clinical Information Extractors” at EMNLP 2022
• Nov. 2022: Three papers at NeurIPS 2022, including
  • Michael presenting “Evaluating Robustness to Dataset Shift via Parametric Robustness Sets”
  • Zeshan presenting “Falsification before Extrapolation in Causal Effect Estimation”
  • Hunter presenting “Training Subset Selection for Weak Supervision”
• July 2022: We have two papers at ICML 2022, with
  • Hunter and Monica presenting “Co-training improves prompt-based learning for large language models”
  • Hussein presenting “Sample Efficient Learning of Predictors that Complement Humans”