We are the
reliable
Artificial
Intelligence
for Health and
Medicine Lab.
Led by Dr. Shalmali Joshi in the Department of Biomedical Informatics (DBMI) at Columbia University in the City of New York.
Our vision is to design AI and ML systems to improve scientific inference and predictive capabilities in the biomedical sciences, discovery, and informatics, focused on challenges of generalizability, reliability, and robustness of inference and prediction. We develop methods that cut across deep learning, reinforcement learning, observational causal inference, and probabilistic modeling.
News
FAccT Best Paper Award
Apara and Sara's paper "A pipeline for enabling path-specific causal fairness in observational health data" received a Best Paper Award at the 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT). Congratulations!
Google Research Scholar Award
Shalmali Joshi is grateful for support from the Google Research Scholar Program!
Benchmarking EHR Foundation Models
We benchmarked SOTA structured EHR foundation models on Columbia University Medical Center data. Read more about 'FoMoH' here!
We are grateful for support from