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Panos Toulis studies causal inference in complex settings, particularly focusing on network structures, resampling methods, and permutation tests. His research emphasizes model-agnostic methodologies that enhance robustness compared to traditional model-based statistical approaches. He is also interested in experimental design within networks and the interface between statistics and optimization. Toulis's work has been published in prominent journals such as the Journal of the Royal Statistical Society, Annals of Statistics, Biometrika, and the Journal of Econometrics. Recognized for his contributions, he received the Arthur P. Dempster Award from Harvard University's Department of Statistics and the LinkedIn Economic Graph Challenge award, in addition to a Google United States/Canada PhD Fellowship in statistics. He earned his PhD in statistics from Harvard University under the supervision of Edo Airoldi, David Parkes, and Don Rubin, and holds an MS in statistics and computer science from Harvard and a BS in electrical and computer engineering from Aristotle University of Thessaloniki, Greece. Toulis has prior experience in software engineering at Google Inc. and various startup companies in Greece.
The doctoral program at Booth is organized into 'dissertation areas' which include Accounting, Behavioral Science, Econometrics and Statistics, Finance, Marketing, and Operations Management.