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 building trustworthy machine learning models for time-to-event prediction (survival analysis) and for time series analysis. I often use nonparametric prediction models that work well under very few assumptions on the data. My main application area is in healthcare.

CoolCrop: I occasionally also work on machine learning for the developing world. I co-founded and now serve as an advisor for CoolCrop, an AgriTech startup based in India that works on providing farmers with cold storage units (such as a refrigerator shared by a village) and market forecasts.

Pre-historic: I obtained my Ph.D. in Electrical Engineering and Computer Science at MIT. 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 for MEET, a program that brings together Israeli and Palestinian high school students to learn computer science and entrepreneurship. I completed my B.S. at UC Berkeley, majoring in Electrical Engineering and Computer Sciences, and Engineering Mathematics and Statistics.

My CV can be found here.

Some Recent News

Regeneron International Science and Engineering Fair (ISEF) (May 7-13, 2022): This is a high school science and engineering fair that I was a finalist in back in 2006 (it was called Intel ISEF then). On May 11, 2022, I was on the Innovation, Entrepreneurship, and Impact panel (along with other ISEF alumni Ulyana Pena, Ashley Sarracino, Matthew Tamayo, and Mo Zerban), answering a variety of questions.

Conference on Health, Inference, and Learning (CHIL) (April 7-8, 2022): I was proceedings chair (with Gerardo Flores and Tom Pollard) [conference website]

KDD workshop on Machine Learning for Consumers and Markets (August 15, 2021): I co-organized a workshop with Wen Wang, Dokyun Lee, and Han Zhao that brings the machine learning and business communities together (a heavy emphasis is on how machine learning translates into actual impact in terms of managerial implications) [workshop website]

NSF CAREER award (June 24, 2021): I received an NSF CAREER award for my proposed project on developing real-time nonparametric machine learning models for healthcare with guarantees

Survival analysis tutorial (June 18, 2021): I taught a survival analysis tutorial at the 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]

Best paper award at AAAI Workshop on AI for Behavior Change 2021 (Feb 8, 2021): research led by my PhD student Emaad Manzoor examines how reputation affects persuasiveness in online debates [arXiv]

Teaching (Spring 2022, mini 4)

94-475/94-775 "Unstructured Data Analytics for Policy"

95-865 "Unstructured Data Analytics" (Sections A4/B4/K4/Z4)

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. I do not take on students who are not already admitted to CMU.

Papers

You can also find my papers listed on Google Scholar.

Some Working Papers

• "Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee"
  George H. Chen
  [arXiv] [code]
  (Under review)
• "Influence via Ethos: On the Persuasive Power of Reputation in Deliberation Online"
  Emaad Ahmed Manzoor, George H. Chen, Dokyun Lee, Michael D. Smith
  [arXiv]
  Best paper at AAAI Workshop on AI for Behavior Change 2021
  (Under review)
• "TOD: Tensor-Based Outlier Detection, a General GPU-Accelerated Framework"
  Yue Zhao, George H. Chen, Zhihao Jia
  [arXiv] [code]
  (Under review)
• "Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate"
  George H. Chen*, Linhong Li*, Ren Zuo, Amanda Coston, Jeremy C. Weiss
  (* = equal contribution)
  Journal paper version of AIME 2020 paper
  (Under review)
  [working draft]

2022

• "ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions"
  Zheng Li*, Yue Zhao*, Xiyang Hu, Nicola Botta, Cezar Ionescu, George H. Chen
  (* = equal contribution)
  IEEE Transactions on Knowledge and Data Engineering, March 2022
  [arXiv] [code]

2021

• "Consumer Behavior in the Online Classroom: Using Video Analytics and Machine Learning to Understand the Consumption of Video Courseware"
  Mi Zhou, George H. Chen, Pedro Ferreira, Michael D. Smith
  Journal of Marketing Research, December 2021
  [SSRN]

2020

• "Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate"
  Linhong Li, Ren Zuo, Amanda Coston, Jeremy C. Weiss, George H. Chen
  International Conference on Artificial Intelligence in Medicine (AIME), August 2020
  [working draft of journal paper version (fixes various bugs in the conference paper version)]
• "Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning"
  Helen Zhou*, Cheng Cheng*, Zachary C. Lipton, George H. Chen, Jeremy C. Weiss
  (* = equal contribution)
  International Conference on Artificial Intelligence in Medicine (AIME), August 2020
  [arXiv] [code]
  (Also presented at the International Conference on Machine Learning (ICML) Workshop on Machine Learning for Global Health, July 2020)
• "Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction Intervals"
  George H. Chen
  Machine Learning for Healthcare (MLHC), August 2020
  [arXiv] [code] [poster]
  Note: This paper is essentially a sequel to my theory paper on nearest neighbor and kernel survival analysis (ICML 2019), where an open problem encountered is how to automatically learn kernel functions for survival analysis aside from using random survival forests.

2019

• "Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption"
  Wei Ma*, George H. Chen*
  (* = equal contribution)
  Neural Information Processing Systems (NeurIPS), December 2019
  [arXiv] [code] [poster] [talk slides]
  Note: We have a longer version in preparation analyzing a collection of missingness probability estimators, with more debiasing guarantees
  Best paper (theoretical track) at INFORMS Data Mining and Decision Analytics Workshop 2019
• "Truck Traffic Monitoring with Satellite Images"
  Lynn H. Kaack, George H. Chen, M. Granger Morgan
  ACM Conference on Computing and Sustainable Societies (COMPASS), July 2019
  [arXiv]
  (Also presented at the International Conference on Machine Learning (ICML) Workshop on Climate Change, June 2019)
• "Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates"
  George H. Chen
  International Conference on Machine Learning (ICML), June 2019
  [arXiv] [code] [talk slides] [poster]
  Note: In my follow-up work at MLHC 2020, I show how to automatically learn kernel functions for survival analysis in a neural net framework, and how to use these kernel functions to help construct survival time prediction intervals.
• "An Interpretable Produce Price Forecasting System for Small Farmers in India using Collaborative Filtering and Adaptive Nearest Neighbors"
  Wei Ma, Kendall Nowocin, Niraj Marathe, George H. Chen
  Information and Communication Technologies and Development (ICTD), January 2019
  [arXiv]

2018

• "Explaining the Success of Nearest Neighbor Methods in Prediction"
  George H. Chen, Devavrat Shah
  Foundations and Trends in Machine Learning, May 2018
  [DOI]

2017

• "Survival-Supervised Topic Modeling with Anchor Words: Characterizing Pancreatitis Outcomes"
  George H. Chen, Jeremy C. Weiss
  Neural Information Processing Systems (NeurIPS) Workshop on Machine Learning for Health (ML4H), December 2017
  [arXiv (short workshop version)]
  (Also presented at the Society for Medical Decision Making North American Meeting, October 2017)
• "Toward Reducing Crop Spoilage and Increasing Small Farmer Profits in India: a Simultaneous Hardware and Software Solution"
  George H. Chen, Kendall Nowocin, Niraj Marathe
  Information and Communication Technologies and Development, November 2017
  [arXiv]

2015

• "A Latent Source Model for Patch-Based Image Segmentation"
  George H. Chen, Devavrat Shah, Polina Golland
  Medical Image Computing and Computer-Assisted Intervention (MICCAI), October 2015
  [arXiv] [paper] [poster]
  Note: For a more comprehensive exposition of this paper, consider reading Chapter 5 of my Ph.D. thesis.
• "Latent Source Models for Nonparametric Inference"
  George H. Chen
  Ph.D. thesis, MIT, May 2015
  [paper]
  Received the George M. Sprowls award for best Ph.D. thesis in Computer Science at MIT
• "Targeting Villages for Rural Development Using Satellite Image Analysis"
  Kush R. Varshney, George H. Chen, Brian Abelson, Kendall Nowocin, Vivek Sakhrani, Ling Xu, Brian L. Spatocco
  Big Data, March 2015
  [paper]

2014

• "A Latent Source Model for Online Collaborative Filtering"
  (alphabetical author ordering) Guy Bresler, George H. Chen, Devavrat Shah
  Neural Information Processing Systems (NeurIPS), December 2014
  [arXiv - longer version] [paper - short conference version] [poster]
  Selected for spotlight (one of 62/1678 submissions)
  Note: An expanded version including intuition for how collaborative filtering relates to an MAP item recommender and derivations for the examples is in Chapter 4 of my Ph.D. thesis; the notation has also been changed to be more similar to the other two papers that went toward my thesis.

2013

• "A Latent Source Model for Nonparametric Time Series Classification"
  (alphabetical author ordering) George H. Chen, Stanislav Nikolov, Devavrat Shah
  Neural Information Processing Systems (NeurIPS), December 2013
  [arXiv - longer version] [paper - short conference version] [poster]
  Note: An expanded version with a lower bound on the misclassification rate and further discussion is in Chapter 3 of my Ph.D. thesis.
• "Sparse Projections of Medical Images onto Manifolds"
  George H. Chen, Christian Wachinger, Polina Golland
  Information Processing in Medical Imaging (IPMI), June-July 2013
  [arXiv] [paper] [poster]

2012

• "Deformation-Invariant Sparse Coding"
  George H. Chen
  Master's thesis, MIT, May 2012
  [paper] [poster]

2011

• "Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain"
  George H. Chen, Evelina G. Fedorenko, Nancy G. Kanwisher, Polina Golland
  Neural Information Processing Systems (NeurIPS) Workshop on Machine Learning and Interpretation in Neuroimaging, December 2011
  [paper] [talk slides]

2010

• "Indoor Localization and Visualization Using a Human-Operated Backpack System"
  Timothy Liu, Matthew Carlberg, George Chen, Jacky Chen, John Kua, Avideh Zakhor
  International Conference on Indoor Positioning and Indoor Navigation (IPIN), September 2010
  [paper]
• "Indoor Localization Algorithms for a Human-Operated Backpack System"
  George Chen, John Kua, Stephen Shum, Nikhil Naikal, Matthew Carlberg, Avideh Zakhor
  International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), May 2010
  [paper]

2009

• "Classifying Urban Landscape in Aerial LIDAR Using 3D Shape Analysis"
  Matthew Carlberg, Peiran Gao, George Chen, Avideh Zakhor
  International Conference on Image Processing (ICIP), November 2009
  [paper]
• "2D Tree Detection in Large Urban Landscapes Using Aerial LIDAR Data"
  George Chen, Avideh Zakhor
  International Conference on Image Processing (ICIP), November 2009
  [paper]
• "Image Augmented Laser Scan Matching for Indoor Dead Reckoning"
  Nikhil Naikal, John Kua, George Chen, Avideh Zakhor
  International Conference on Intelligent Robots and Systems (IROS), October 2009
  [paper]

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Last updated 7/7/2022.