An algorithmic safety view of learning in healthcare
Speaker: Shalmali Joshi, Harvard University
Location: 60 Fifth Avenue 150
Date: March 9, 2022, 2 p.m.
Host: Rajesh Ranganath
Machine Learning advances have revolutionized many domains such as machine translation, complex game playing, and scientific discovery. On the other hand, ML has only enjoyed modest successes in healthcare. To improve the utility, reliability, and robustness of Machine Learning (ML) models in human-centered domains such as health, we need to address several foundational challenges. In this talk, I will demonstrate how an algorithmic-safety perspective can motivate specific technical challenges for learning in healthcare. Specifically, I will discuss the need to improve the utility of ML-robustness, explainability with an emphasis on decision-making, and post-hoc algorithmic safety to prevent harm. I will discuss my contributions on i) aiding safe decision-making in non-IID settings using time-series explainability intended to address clinicians’ requirements, ii) novel learning algorithms to optimize for safety in sequential decision-making settings, and iii) methods to improve causal robustness of ML methods designed for practical generative settings. I will conclude with an overview of my future research vision on designing novel objectives for expanding ML-based solutions to general and practical generative settings we encounter in health and outlining an experimental design framework for validating ML models targeting these objectives.
Shalmali Joshi is a Postdoctoral Fellow at the Center for Research on Computation and Society at Harvard University. Previously, she was 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 and has served as the communications chair for ACM CHIL 2020, besides reviewing and meta-reviewing for several ML academic venues.
In-person attendance only available to those with active NYU ID cards.