Stochastic Gradient Estimation: Tutorial Review, Recent Research - Dr Michael Fu

Program:

Stochastic gradient estimation techniques are methodologies for deriving computationally efficient estimators used in simulation optimization and sensitivity analysis of complex stochastic systems that require simulation to estimate their performance. Using a simple illustrative example, the three most well-known direct techniques that lead to unbiased estimators are presented: perturbation analysis, the likelihood ratio (score function) method, and weak derivatives (also known as measure-valued differentiation). A few real-world applications are discussed and then some recent research is summarized.

Speaker:

Michael C. Fu is Ralph J. Tyser Professor of Management Science in the Decision, Operations, and Information Technologies department of the Robert H. Smith School of Business, with a joint appointment in the Institute for Systems Research and an affiliate appointment in the Department of Electrical & Computer Engineering (both in the Clark School of Engineering), all at the University of Maryland, College Park. He received degrees in mathematics and EECS from MIT, and his Ph.D. in applied mathematics from Harvard University. His research interests include simulation optimization and applied probability, particularly with applications towards supply chain management and financial engineering. In 2004 he was named a Distinguished Scholar-Teacher at the University of Maryland. He served as Program Chair for the 2011 Winter Simulation Conference. From September 2010 through August 2012, he served as the Operations Research Program Director at the National Science Foundation. He is a Fellow of INFORMS and IEEE.

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