I’m a statistical machine learning researcher at Microsoft Research New England and an adjunct professor of Statistics at Stanford University. Before joining Microsoft, I spent three wonderful years as an assistant professor of Statistics and, by courtesy, Computer Science at Stanford University. Prior to this, I spent a year as a Simons Math+X postdoctoral fellow, working with Emmanuel Candes. 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. My Ph.D. advisor was Mike Jordan, and my undergraduate research advisors were Maria Klawe and David Walker.
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, weather and 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.
If you are a PhD student interested in interning with me at Microsoft Research New England, please apply here. I typically begin to review applications in December.
For more details about my interests and work please see my external website.