I am an assistant professor at Carnegie Mellon University's Heinz College of Public Policy and Information Systems. I work on machine learning for social data analytics, healthcare, and infrastructure development. I am particularly interested in developing nonparametric inference methods and providing theoretical guarantees for why they work.
I am looking for PhD students! If you have already been admitted to CMU, feel free to contact me to discuss!
Before joining Carnegie Mellon, I finished my Ph.D. in Electrical Engineering and Computer Science at MIT, advised by Polina Golland and Devavrat Shah. My thesis developed theory for forecasting viral news, recommending products to people, and finding human organs in medical images. I also worked on satellite image analysis to help bring electricity to rural India, and modeled brain activation patterns evoked by reading sentences. Between grad school and becoming faculty, I helped develop the recommendation engine at a predictive analytics startup Celect and then was a teaching postdoc in MIT's Digital Learning Lab, where I was the primary course developer and instructor for a new edX course on computational probability and inference.
I enjoy teaching and pondering the future of education! I have previously taught at MIT, UC Berkeley, and in Jerusalem at program MEET that brings together Israeli and Palestinian high school students. As a grad student, I served on the Task Force on the Future of MIT Education, and my time as a teaching postdoc was all about better understanding the digital learning space.
Last updated January 15, 2017. Photo credit: Danica Chang.