Shalmali Joshi is an Assistant Professor of Biomedical Informatics at Columbia University. Previously, she was a Postdoctoral Fellow at the Center for Research on Computation and Society at Harvard University, as well as a Postdoctoral Fellow at the Vector Institute. She received her Ph.D. from the University of Texas at Austin (UT Austin). Her research is on the algorithmic safety of Machine Learning for human-centered domains. Shalmali has contributed to the field of explainability, robustness, and novel algorithms for ML safety with an emphasis on practical generative settings and impact on decision-making. Shalmali has published in ML and inter-disciplinary venues in healthcare such as NeurIPS, FAccT, CHIL, MLHC, PMLR, and perspectives in JAMIA, LDH, and Nature Medicine. She has co-founded the Fair ML for Health NeurIPS workshop, General Chair for ML4H 2022, and Program Chair for MLHC 2022.