Livestream over Zoom: https://mit.zoom.us/j/94112513921
Abstract: Many decision models involve both parameter estimation and optimization over a set of decision variables. Asymptotic properties of the estimators are often used to justify a model’s validity, but these results may not be applicable in high dimensions. This talk will discuss the potential bias that arises in these optimization settings and potential approaches to remove this bias in high-dimensional models.
Biosketch: John R. Birge is the Hobart W. Williams Distinguished Service Professor of Operations Management at the University of Chicago Booth School of Business. He studies mathematical modeling of systems under uncertainty, especially for maximizing operational and financial goals using the methodologies of stochastic programming and large-scale optimization. He has applied his work in a variety of contexts and industries including energy, finance, health care, manufacturing, and transportation. He has published widely and is the recipient of the Best Paper Award from the Japan Society for Industrial and Applied Mathematics, the Institute for Operations Research and the Management Sciences Fellows Award, the Institute of Industrial Engineers Medallion Award and was elected to the National Academy of Engineering.
A former dean of the Robert R. McCormick School of Engineering and Applied Sciences at Northwestern University, he has worked as a consultant for a variety of firms including the University of Michigan Hospitals, Deutsche Bank, Allstate Insurance Company, and Morgan Stanley. Birge earned a bachelor’s degree in mathematics from Princet and a master’s degree and a PhD in operations research from Stanford University.