Matt Taddy is Associate Professor of Econometrics and Statistics and Neubauer Family Faculty Fellow 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 'Data Mining' course at Chicago Booth.
Taddy works on building robust solutions for large scale data analysis problems. This involves dimension reduction techniques for massive datasets and development of models for inference on the output of these algorithms. Applications are ongoing in consumer database mining, digital marketing, analysis and optimization of computer simulators, and in text mining for analysis of social media, financial news, and political speech. 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.
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.
2013 - 2014 Course Schedule
Text data analysis and sentiment mining; network analysis; sparse inverse regression and dimension reduction; tree-based reasoning for regression and classification; Bayesian nonparametric methods and inference for space-time processes.