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.

Bio: John R. Birge is the Jerry W. and Carol Lee Levin Distinguished Service Professor of Operations Management. 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 was first drawn to this area by a need to use mathematics in a useful and practical way. "My research has shown how special problem structure can allow for efficient solution of complex problems of decision making under uncertainty," Birge explains. This 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.