Matt Taddy is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business, a fellow of the Computation Institute, and a Principal Researcher at Microsoft Research New England. He works at the intersections of statistics, economics, and machine learning. His research is directed towards the development of new algorithms for machine learning, uncertainty quantification for these algorithms, and the incorporation of machine learning and artificial intelligence ideas into the study of social and economic systems. Recent projects include study of the polarization of political dialogue, modeling for massive consumer demand and incentive systems, and adaptation of artificial intelligence techniques towards questions of causation.
Taddy developed and teaches the Big Data class at Booth, an advanced MBA course that is designed to prepare students for careers at the interface of business strategy and Data Science. He has collaborated extensively with national laboratories, a variety of start-up ventures, and was a research fellow at eBay from 2014-2016. He earned his PhD in Applied Mathematics and Statistics in 2008 from the University of California, Santa Cruz, as well as a BA in Philosophy and Mathematics and an MSc in Mathematical Statistics from McGill University. He joined the Chicago Booth faculty in 2008.