Michael Cooper
Ph.D. Student, Computer Science
University of Toronto
Welcome to my website! I'm Michael - I'm a Ph.D. student in Computer Science at the University of Toronto, advised by Rahul G. Krishnan, and Michael Brudno. My research focuses on designing clinical machine learning systems for implementation in national-scale, high-stakes decision-making settings like liver transplant prioritization. In this same vein, I also design and study algorithms to make modern machine learning methods more reliable and interpretable.
Previously, I earned B.S. and M.S. degrees in Computer Science from Stanford University, where I had the privilege of investigating the influence of indoor built space design on human wellbeing with James Landay and Sarah Billington, building an augmented reality application to align patients and medical images in 3D space with Bruce Daniel, and constructing a computer vision dataset comprising complex multi-object multi-actor scenes with Ehsan Adeli and Fei-Fei Li.
In my free time, I enjoy scuba diving, long-distance running, alpine skiing, and reading science fiction and historical non-fiction (a few favorites: Liu Cixin's Remembrance of Earth's Past Trilogy, Kim Stanley Robinson's Mars Trilogy, and Ben Rich and Leo James' Skunk Works).
( May 2025 ) Our paper, Red Teaming Large Language Models for Healthcare, summarizes the design process and findings of our 2024 workshop at MLHC. Our identified vulnerabilities—hallucination, syncophany, misprioritization, and more—motivate future study and cautious deployment of language models in high-stakes healthcare settings.
( January 2025 ) Excited to be starting a research internship at Abridge, working on generative models for clinical documentation!
( August 2024 ) We will be hosting a workshop on Red Teaming Large Language Models in Healthcare at this year's Machine Learning for Healthcare (MLHC) conference! Come check it out on August 15!
- M. Cooper, X. Gao, X. Zhao, D. Khoroshchuk, et al. “DynaMELD: A Dynamic Model of End-Stage Liver Disease for Equitable Prioritization,” medRxiv preprint, 2024.
- A. Gharari*, M. Cooper*, R. Greiner, RG. Krishnan. “Copula-based Deep Survival Models for Dependent Censoring,” Uncertainty in Artificial Intelligence (UAI), 2023.
- M. Cooper*, Z. Ji*, RG. Krishnan. “Machine Learning in Computational Histopathology: Challenges and Opportunities,” Genes, Chromosomes, and Cancer, 2023.