I’m a statistical machine learning researcher at Microsoft Research New England and an adjunct professor at Stanford University. I received my Ph.D. in Computer Science (2012) and my M.A. in Statistics (2011) from UC Berkeley and my B.S.E. in Computer Science (2007) from Princeton University. Before joining Microsoft, I 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. My Ph.D. advisor was Mike Jordan, and my undergraduate research advisors were Maria Klawe and David Walker. I got my first taste of research at the Research Science Institute and learned to think deeply of simple things at the Ross Program.
My current research interests include statistical machine learning, scalable algorithms, high-dimensional statistics, approximate inference, and probability. Lately, I’ve been developing and analyzing scalable learning algorithms for healthcare, climate forecasting, approximate posterior inference, high-energy physics, recommender systems, and the social good.
Quixotic though it may sound, I hope to use computer science and statistics to change the world for the better. If you have thoughts on how to do this, feel free to contact me.
For more details about my interests and work please see my external website.