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David Duvenaud is an associate professor at the University of Toronto, specializing in artificial intelligence and machine learning. His research focuses on Artificial General Intelligence (AGI) governance, evaluation methodologies, and mitigating catastrophic risks associated with future systems. Following an extended sabbatical with the Alignment Science team at Anthropic, he has delved into various topics including deep probabilistic models, Neural Ordinary Differential Equations (Neural ODEs), and automatic chemical design utilizing generative models. His previous postdoctoral work was at the Harvard Intelligent Probabilistic Systems group under the guidance of Ryan Adams. David holds a Ph.D. from the University of Cambridge and an M.Sc. from the University of British Columbia. He is a founding member of the Vector Institute and a Sloan Research Fellow, holding the Schwartz Reisman Chair in Technology and Society.
University of Toronto • Toronto, ON, Canada
Engaging in research and teaching in computer science with a focus on AGI and machine learning.
Department of Sociology