Associate Professor of Econometrics and Statistics, and John E. Jeuck Faculty Fellow
Panos Toulis studies causal inference in complex settings (e.g., networks) through resampling methods such as permutation tests. These methods are model-agnostic and thus have a degree of robustness not afforded by classical model-based statistlcal methods. He is also interested in the design of experiments on networks, and generally the interface between statistics and optimization.
His research has been published in the Journal of the Royal Statistical Society, Annals of Statistics, Biometrika, Journal of the Americal Statistical Association, Journal of Econometrics, Statistics and Computing, and Games and Economic Behavior, as well as in major machine learning and economics conferences. For his research, Toulis has received the Arthur P. Dempster Award from Harvard University’s Department of Statistics, the LinkedIn Economic Graph Challenge award, and the 2012 Google United States/Canada PhD Fellowship in statistics.
Toulis got his PhD in statistics from Harvard University, advised by Edo Airoldi, David Parkes, and Don Rubin. He also holds MS degrees in statistics and computer science from Harvard University, and a BS in electrical and computer engineering from Aristotle University in Thessaloniki, Greece. Outside of academia, he has prior corporate experience in software engineering at Google Inc. and at startup companies in Greece. He also enjoys science fiction, history, and politics.
Machine learning is being tasked with an increasing number of important decisions. But the answers it generates involve a degree of uncertainty.
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Identifying how experimental units interact, and testing how these relationships affect outcomes, can make experiments more informative and valuable.
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Chicago Booth's Panos Toulis suggests a statistical method that can help officials arrive at more accurate infection counts.
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