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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 primarily work on machine learning for healthcare, with an emphasis on forecasting problems involving survival analysis as well as time series data. A recurring theme in my work is the use of nonparametric prediction methods that aim to make 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 identify forecast evidence to help practitioners make decisions.

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

CoolCrop: I occasionally also work on machine learning for the developing world. I am a co-founder and advisor for CoolCrop, an AgriTech startup based in India that works on providing cold storage units (e.g., a refrigerator shared by a village) to farmers and also providing market forecasts to help farmers make decisions on business operations.

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

June 18, 2021: I'm teaching a survival analysis tutorial at the 2021 SIGMETRICS conference (this tutorial is based on a previous tutorial with more of a healthcare focus that I co-taught with Jeremy Weiss at CHIL 2020): [tutorial webpage]

Teaching (Spring 2021)

94-775 "Unstructured Data Analytics for Policy" (mini 4)

95-865 "Unstructured Data Analytics" (mini 4)

Research Group

I've had the fortune of working with many wonderful students over the years (listed below). If you're interested in working with me and you already are a CMU student, then feel free to shoot me an email telling me what you're particularly excited about working on, why it overlaps with my research interests, and what skills you've already cultivated (if you're a master's student or an undergrad, please complete several machine learning and statistics courses prior to contacting me). I do not take on students who are not already admitted to CMU.

Current PhD students:

Past students and where they went after graduating:

  • Emaad Manzoor (PhD 2021), Assistant Professor at UW Madison School of Business starting in Fall 2021
  • Mi Zhou (PhD 2020), Assistant Professor at UBC Sauder School of Business
  • Wei Ma (master's in ML 2018/PhD 2019), Assistant Professor at Hong Kong Polytechnic University in the Department of Civil and Environmental Engineering
  • Lynn H. Kaack (master's in ML 2018/PhD 2019), postdoc at ETH Zurich in the Energy Politics Group
  • Xiaotong (Maggie) Lu (MISM 2020), McKinsey
  • Runtong (Fred) Yang (MISM 2019), Capitol One
  • Ren Zuo (MISM 2018), Cornerstone Research
  • Linhong (Lexie) Li (B.S. 2020), McKinsey
  • Junyan Pu (B.S. 2020), CMU master's degree program in CS
♣ indicates a PhD student who worked with me on a secondary master's in ML (I was their master's research advisor but not their PhD research advisor)

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 2/25/2021.