George Chen is an assistant professor of information systems at Heinz College and an affiliated faculty member of the Machine Learning Department. He works on machine learning for healthcare and for information systems in developing countries. In these applications, his work revolves around forecasting, such as predicting how long a patient will stay in a hospital, or when and where farmers in rural India should sell their crops. To produce forecasts, George typically uses nonparametric methods that, instead of specifying a model for the data in advance, let the data decide on what model to use, essentially through an election-like process where each data point casts a vote. Since these methods inform interventions that can be costly and affect people's well-being, ensuring that predictions are reliable and interpretable is essential. To this end, in addition to developing nonparametric predictors, George also produces theory for when and why they work, and identifies forecast evidence that would be helpful to practitioners for decision making.
George obtained his S.M. (2012), E.E. (2014), and Ph.D. (2015) degrees from the Electrical Engineering and Computer Science department at MIT, where he was supported by an NSF fellowship, an NDSEG fellowship, and a Siebel Scholarship. His Ph.D. dissertation on nonparametric machine learning methods for analyzing social data and medical images won MIT's George Sprowls award for best thesis in computer science. He previously completed his B.S. (2010) at UC Berkeley with dual majors in Electrical Engineering and Computer Sciences, and Engineering Mathematics and Statistics. He enjoys teaching and has taught for courses at UC Berkeley, MIT, and in Jerusalem at a summer program MEET that brings together Israeli and Palestinian high school students. George has won teaching awards at both UC Berkeley and MIT, including MIT's top teaching award for graduate students, the Goodwin Medal (2015).