iSchool Distinguished Lecture: John R. Birge

Abstract: The explosion of data acquisition and AI methodology promises to revolutionize all sectors of the global economy by rapidly replacing human tasks including decision making. In many contexts, inherent limitations on the availability of data and the optimizing nature of AI impose boundaries on the effectiveness of these new tools. This talk will discuss these boundaries on the best achievable uses of data and how diversity in modeling can often yield gains over larger monolithic models.

Bio: John R. Birge 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 was first drawn to this area by a need to use mathematics in a useful and practical way. His research has been supported by the National Science Foundation, the Ford Motor Company, General Motors Corporation, the National Institute of Justice, the Office of Naval Research, the Electric Power Research Institute, and Volkswagen of America. 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, and he uses cases from these experiences in his teaching. Birge earned a bachelor's degree in mathematics from Princeton University and a master's degree and PhD in operations research from Stanford University.