UTCS Colloquia/AI - Warren Powell/Princeton University, "Unifying the Jungle of Stochastic Optimization," PAI 3.14

Contact Name: 
Eliana Feasley
PAI 3.14
Sep 21, 2012 11:00am - 12:00pm

Sign-up Schedule: http://apps.cs.utexas.edu/talkschedules/cgi/list_events.cgi

Type of Talk: AI Colloquia

Speaker/Affiliation: Warren Powell/Princeton University

Talk Audience: Faculty, Graduate Students, Undergraduate Students, Outside Interested Parties

Date/Time: 09/21/2012, 11:00 AM to 12:00 PM

Location: PAI 3.14

Host: Peter Stone

Talk Title: Unifying the Jungle of Stochastic Optimization

Talk Abstract: The variety of applications and computational challenges in stochastic optimization has created a diverse set of communities that use names such as Markov decision processes, stochastic programming, approximate dynamic programming, reinforcement learning, stochastic search, simulation-optimization, and stochastic control. Dividing these communities are differences in terminology and notation, although the more subtle differences are in the motivating applications which includes issues such as scalar vs. vector-valued decisions, and model-based vs. model-free applications. In this talk, I will provide a modeling framework for sequential decision problems, and use this to unify several major fields by identifying four fundamental classes of policies. I will then create bridges between several communities, identifying similarities hidden by differences in notation and terminology, as well as important differences.

Speaker Bio: Warren B. Powell is a professor in the Department of Operations Research and Financial Engineering at Princeton University, where he has taught since 1981. He is the director of CASTLE Laboratory (http://www.castlelab.princeton.edu), which specializes in the development of stochastic optimization models and algorithms with applications in transportation and logistics, energy, health and finance. His work in transportation produced a network planning model that was used by the entire LTL trucking industry, and a real-time dispatch model for truckload trucking that is being used to dispatch over 65,000 trucks. He pioneered the development of approximate dynamic programming for high-dimensional resource allocation problems that is being used in rail, truckload trucking, the Air Force, and for the management of spare parts for aircraft. He recently established the Princeton Laboratory for Energy Systems Analysis (http://energysystems.princeton.edu) to take this work into the area of energy systems. The author/coauthor of over 180 refereed publications, he is an Informs Fellow, and the author of Approximate Dynamic Programming: Solving the curses of dimensionality, and coauthor of Optimal Learning (both published by Wiley). He is a recipient of the Wagner prize, and has twice been a finalist in the prestigious Edelman competition. He has also served in a variety of editorial and administrative positions for Informs, including Informs Board of Directors, Area Editor for Operations Research, President of the Transportation Science Section, and numerous prize and administrative committees.