Machine Learning for Applications in Economics and Finance
William H. Abbott Professor of International Business and Economics, University of Chicago Booth School of Business and Exco Member and Senior Fellow of ABFER
Professor Renée Adams
AGSM Scholar, Professor of Finance, Commonwealth Bank Chair in Finance, University of New South Wales and Exco Member and Senior Fellow of ABFER
Professor Matt Taddy
Professor of Econometrics and Statistics, University of Chicago
Principal Researcher, Microsoft Research
Matt Taddy joins Microsoft Research from the University of Chicago, where he is Professor of Econometrics and Statistics at the Booth School of Business and a fellow of the Computation Institute. He leads MSR’s Alice project on Economic AI.
Taddy works at the intersections of statistics, economics, and machine learning. His research is directed towards development of new algorithms for machine learning, uncertainty quantification for these algorithms, and incorporation of artificial intelligence into the study of social and economic systems. Recent projects include optimization for complex demand and incentive systems, analysis of the polarization of political dialogue, and development of artificial intelligence for 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 and Microsoft in 2016.