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George H. Chen

Assistant Professor of Information Systems, Heinz College
Affiliated Faculty, Machine Learning Department
Carnegie Mellon University

Email: georgechen [at symbol] cmu.edu

Office: HBH 2216 (the west wing of Hamburg Hall, second floor)

About

I work on forecasting problems in healthcare and in sustainable development, such as predicting how long a patient will stay in a hospital, or how produce prices will change in a week at over a thousand Indian markets. To produce forecasts, I typically use nonparametric methods that make very few assumptions on the underlying data. Since these methods inform interventions that can be costly and affect people's well-being, ensuring that predictions are reliable is essential. To this end, in addition to developing nonparametric predictors, I also produce theory to understand when and why they work, and I identify forecast evidence to help practitioners make decisions.

Research areas: nonparametric prediction, survival analysis, time series forecasting, missing data, healthcare, sustainable development

Pre-historic: I obtained my Ph.D. in Electrical Engineering and Computer Science at MIT, advised by Polina Golland and Devavrat Shah. My thesis was on nonparametric machine learning methods. At MIT, I also worked on satellite image analysis to help bring electricity to rural India, and taught twice in Jerusalem at a program MEET that brings together Israeli and Palestinian high school students to learn computer science and entrepreneurship. Between grad school and becoming faculty, I helped develop the recommendation engine at a predictive analytics startup Celect (since acquired by Nike) and then was a teaching postdoc in MIT's Digital Learning Lab, where I was the primary instructor and course developer for an edX course on computational probability and inference. I completed my undergraduate studies at UC Berkeley, dual majoring in Electrical Engineering and Computer Sciences, and Engineering Mathematics and Statistics.

My CV can be found here.

Survival Analysis Tutorial

July 23, 2020: Together with Jeremy Weiss, we are teaching a tutorial on survival analysis at the 2020 Conference on Health, Inference, and Learning (CHIL): [tutorial webpage]

Teaching (academic year 2019-2020)

95-865 "Unstructured Data Analytics" (minis 2 and 3)

94-775 "Unstructured Data Analytics for Policy" (mini 3)

Papers

You can also find my papers listed on Google Scholar.

Working Papers

2020

2019

2018

2017

2015

2014

2013

2012

2011

2010

2009


Last updated 7/27/2020.