Presented by: The Education Working Group at The Data Science Institute, Columbia University

Guest Speaker: Dr. Marzyeh Ghassemi, Assistant Professor in Computer Science and Medicine, University of Toronto

Talk Title: Don’t Expl-AI-n Yourself: Exploring "Healthy" Models in Machine Learning for Health

Talk Abstract: Despite the importance of human health, we do not fundamentally understand what it means to be healthy. Health is unlike many recent machine learning success stories - e.g., games or driving - because there are no agreed-upon, well-defined objectives. In this talk, Dr. Marzyeh Ghassemi will discuss the role of machine learning in health, argue that the demand for model interpretability is dangerous, and explain why models used in health settings must also be "healthy". She will focus on a progression of work that encompasses prediction, time series analysis, and representation learning.

Bio: Dr. Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She will be moving to MIT's EECS/IMES in July 2021. She has served as a NeurIPS 2019/2020 Workshop Co-Chair, and General Chair for the ACM Conference on Health, Inference and Learning (ACM CHIL). Previously, she was a Visiting Researcher with Alphabet's Verily and a post-doc with Dr. Peter Szolovits at MIT. Prior to her PhD in Computer Science at MIT, Dr. Ghassemi received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. Her work has been featured in popular press such as MIT News, NVIDIA, Huffington Post. She was also recently named one of MIT Tech Review’s 35 Innovators Under 35.