Hi there! Thanks for stopping by. A little bit about me: I’m currently a Ph.D. student in the Machine Learning Department at Carnegie Mellon University (planning on graduating ≈summer 2019), where I’m advised by Ryan Tibshirani and Zico Kolter. My research interests are pretty broad, but they include high-dimensional statistics, convex optimization, implicit regularization, and nonparametric regression.
Before starting grad school, I worked at Microsoft, where I managed and worked on teams doing machine learning in order to improve Bing’s ranking and query rewriting systems. I also spent a little bit of time in the Department of Statistics at the (nearby) University of Washington, looking into some interesting statistical and computational questions related to ranking.
|Aug 24, 2018||I (successfully) proposed my thesis. The title of my thesis is: Statistical and Computational Properties of Some “User-Friendly” Methods for High-Dimensional Estimation. Thanks to my committee (Siva Balakrishnan, John Duchi, Zico Kolter, Ameet Talwalkar, and Ryan Tibshirani) for all their help.|
|Dec 20, 2016||I was a teaching assistant for Carnegie Mellon’s course on convex optimization.|
|Sep 26, 2016||I was featured on a podcast by the World Affairs Council, on how data science can help with social problems. Feel free to have a listen here (≈30 mins long)!|
|Sep 12, 2016||I was on the steering committee for the interpretable machine learning workshop, at NIPS 2016.|