Jeffrey’s research focuses on questions of reliability and robustness for statistical and machine learning methods. His work touches on diverse topics including generalization bounds, sequential decision making, learning theory, and Markov chain Monte Carlo. Jeff holds a Ph.D. and an M.Sc. in Statistical Sciences from the University of Toronto, where he was a Vanier Scholar, and a B.Math. from the University of Waterloo. Jeff was a visiting student at the Institute for Advanced Study in 2020, and was recognized as a Rising Star in Data Science by the University of Chicago’s Data Science Institute in 2021.