Panos Toulis studies causal inference in complex settings, such as networks or multi-agent economies. He is also interested in the interface of statistics and optimization, particularly in inference problems on large data sets through stochastic gradient descent.
His research has been published in the Annals of Statistics, Journal of Statistical Software, Games and Economic Behavior, and Statistics and Computing, and 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 Don Rubin, David Parkes, and Edo Airoldi. 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.