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NIPS 2017 workshop "Nearest Neighbors for Modern Applications with Massive Data": Together with Christina Yu and Devavrat Shah, I am co-organizing a NIPS workshop on nearest neighbor methods! Despite these methods being old (appearing in text as early as the 11th century in the Book of Optics by Alhazen), they continue to be popular, often used as part of a bigger prediction machine. Moreover, surprisingly our understanding of them is still quite incomplete. We're gathering practitioners and theoreticians alike to get up to speed on the latest developments. For more info, including poster presentation submission instructions, check out the workshop website.

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 infrastructure 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:

I'm slated to be teaching 95-865 "Unstructured Data Analytics" in Fall 2017.

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 October 12, 2017. Photo credit: Danica Chang.