Matt Taddy is Associate Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research is focused on statistical methodology and data mining, driven by applications in business and engineering. He developed and teaches the MBA 'Big Data' course at Chicago Booth.
Taddy works on building robust solutions for large scale data analysis problems, at the interface of econometrics and machine learning. This involves dimension reduction techniques for massive datasets and development of models for inference on the output of these algorithms. He has collaborated both with small start-ups and with large research agencies, including NASA Ames, and Lawrence Livermore, Sandia, and Los Alamos National Laboratories, and is a research fellow at eBay.
Taddy earned his PhD in Applied Math 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.
2015 - 2016 Course Schedule