Lester Mackey is a statistical machine learning researcher at Microsoft Research New England. Before joining Microsoft, Mackey spent three wonderful years as an assistant professor of Statistics and (by courtesy) Computer Science at Stanford and one as a Simons Math+X postdoctoral fellow, working with Emmanuel Candes. Mackey received his Ph.D. in Computer Science (2012) and his M.A. in Statistics (2011) from UC Berkeley and his B.S.E. in Computer Science (2007) from Princeton University. His Ph.D. advisor was Mike Jordan, and his undergraduate research advisors were Maria Klawe and David Walker.
Mackey’s current research interests include statistical machine learning, scalable algorithms, high-dimensional statistics, approximate inference, and probability. Lately, he has been developing and analyzing scalable learning algorithms for healthcare, climate forecasting, approximate posterior inference, high-energy physics, recommender systems, and the social good.