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About

I am an assistant professor at Carnegie Mellon University's Heinz College of Public Policy and Information Systems, and an affiliated faculty member of the Machine Learning Department. I primarily work on machine learning for healthcare, and information and communication technologies for development. A recurring theme across my work is the use of nonparametric prediction methods in solving temporal or spatial forecasting problems. Since these methods inform interventions that can be costly and affect people’s well-being, ensuring that predictions are interpretable is essential.

Current projects:

Spring 2018: I'm currently teaching 95-865 "Unstructured Data Analytics" and 94-775 "Unstructured Data Analytics for Policy".

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 instructor and course developer 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 a 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 February 3, 2018. Photo credit: Danica Chang.