reAIM Lab

reliable Artificial Intelligence for Health and Medicine


PH-20 402

622 W 168th St

New York, NY 10032

Shalmali Joshi is an Assistant Professor at Columbia University, Department of Biomedical Informatics where she leads the reAIM Lab. She works to advance Machine Learning (ML) and Artificial Intelligence (AI) methods with the goal of advancing clinical health and medicine for all.

Artificial Intelligence and Machine Learning, if done right, have a huge potential for transforming health, medicine and improving health equity. reAIM's mission is to refocus methodological developments in Machine Learning and Artificial Intelligence to make ML/AI reliable and safe for health, medicine, and improve health inequities along the way.

Our group develops methods using causal inference, off-policy reinforcement learning, and other advanced deep and machine learning, combined with an in-depth understanding of the clinical problem to develop safe, robust, generalizable learning-based solutions.


May 5, 2023 Our paper on “Why did the model fail?: Attributing model performance change to distribution shifts” is accepted to ICML!
Mar 18, 2023 Our paper on “Learning-to-defer for sequential medical decision-making under uncertainty” is accepted to TMLR!
Mar 12, 2023 I am looking for Postdocs for Summer/Fall 2023!
Feb 2, 2023 I started as an Assistant Professor at Columbia DBMI! :sparkles:
Oct 22, 2022 I gave a lecture in a grad class on Causal Inference, Explainability, and Fairness in ML at UT Austin
Oct 4, 2022 I served as a panelist on AI in South Asia, Lakshmi Mittal Institute, Harvard University