Student-Faculty Dinner Discussion, Uncertainty and Energy System Modeling – Warren Powell
Speaker: Warren Powell, Professor of Operations Research and Financial Engineering
Warren Powell, a professor in Princeton’s Department of Operations Research and Financial Engineering, has pioneered the use of approximate dynamic programming for high-dimensional applications in freight transportation. He is the founder and director of the laboratory for Computational Stochastic Optimization and Learning (CASTLE Labs), which contributes to models and algorithms in stochastic optimization with applications for energy systems, transportation, health and medical research, business analytics, and the laboratory sciences. Powell’s projects have twice been recognized as a finalist for INFORMS’ Franz Edelman Award, and one won the 2009 Daniel Wagner Prize for Excellence in Operations Research Practice. This research led him into the field of optimal learning for optimizing expensive functions using the knowledge gradient. As an INFORMS Fellow, he has served in a range of service positions, including at the Society for Transportation and Logistics, the INFORMS Computing Society and the INFORMS Optimization Society. He has written two books and more than 200 papers, and is working on a new book “Optimization Under Uncertainty: A Unified Framework.” He has advised 50 graduate students and postdocs, and has supervised almost 200 undergraduate theses. Powell joined Princeton’s faculty in 1981 after receiving his BSE from Princeton and his Ph.D. from the Massachusetts Institute of Technology.
Student-Faculty Dinner Discussion, Uncertainty and Energy System Modeling – Warren Powell
Event Date
Tue, Nov 14, 2017 ・ 6:00 PM - 8:00 PM
Location
Prospect House, Room E
Speaker: Warren Powell, Professor of Operations Research and Financial Engineering
Warren Powell, a professor in Princeton’s Department of Operations Research and Financial Engineering, has pioneered the use of approximate dynamic programming for high-dimensional applications in freight transportation. He is the founder and director of the laboratory for Computational Stochastic Optimization and Learning (CASTLE Labs), which contributes to models and algorithms in stochastic optimization with applications for energy systems, transportation, health and medical research, business analytics, and the laboratory sciences. Powell’s projects have twice been recognized as a finalist for INFORMS’ Franz Edelman Award, and one won the 2009 Daniel Wagner Prize for Excellence in Operations Research Practice. This research led him into the field of optimal learning for optimizing expensive functions using the knowledge gradient. As an INFORMS Fellow, he has served in a range of service positions, including at the Society for Transportation and Logistics, the INFORMS Computing Society and the INFORMS Optimization Society. He has written two books and more than 200 papers, and is working on a new book “Optimization Under Uncertainty: A Unified Framework.” He has advised 50 graduate students and postdocs, and has supervised almost 200 undergraduate theses. Powell joined Princeton’s faculty in 1981 after receiving his BSE from Princeton and his Ph.D. from the Massachusetts Institute of Technology.
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