Shalmali Joshi received her Ph.D. from the University of Texas at Austin (UT Austin) in 2018, where she was advised by Schlumberger Centennial Chair Professor of Electrical and Computer Engineering, Joydeep Ghosh. Shalmali's research goal is to make Machine Learning (ML) more reliable for clinical healthcare. She uses principles of probabilistic modeling, causal inference, and algorithmic fairness to inform generalization, explainability, and equity of ML in health. She is passionate about improving transparency and accountability in ML and ensuring the benefits of ML are equitable in clinical practice. She has contributed several talks and commentaries regarding these for researchers and practitioners interested in applying ML for health. She is also the founding co-chair of the first workshop on Fairness in ML for Health, at NeurIPS, one of the largest annual ML conferences.