Main content start
Peter Glynn
Thomas W. Ford Professor in the School of Engineering and Professor, by courtesy, of Electrical Engineering
Department
Management Science and Engineering
CV Link
PhD, Stanford University, Operations Research (1982)
B.Sc (Hon), Carleton University, Mathematics (1978)
Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University, and also holds a courtesy appointment in the Department of Electrical Engineering.. He received his Ph.D in Operations Research from Stanford University in 1982. He then joined the faculty of the University of Wisconsin at Madison, where he held a joint appointment between the Industrial Engineering Department and Mathematics Research Center, and courtesy appointments in Computer Science and Mathematics. In 1987, he returned to Stanford, where he joined the Department of Operations Research. From 1999 to 2005, he served as Deputy Chair of the Department of Management Science and Engineering, and was Director of Stanford's Institute for Computational and Mathematical Engineering from 2006 until 2010. He served as Chair of MS&E from 2011 through 2015. He is a Fellow of INFORMS and a Fellow of the Institute of Mathematical Statistics, has been co-winner of Best Publication Awards from the INFORMS Simulation Society in 1993 and 2008, was a co-winner of the Best (Biannual) Publication Award from the INFORMS Applied Probability Society in 2009, and was the co-winner of the John von Neumann Theory Prize from INFORMS in 2010. In 2012, he was elected to the National Academy of Engineering.
His research centers on computational algorithms, mathematical approximations, statistical methodology, and optimization methods for the analysis of systems in which uncertainty is present. He has developed algorithms that are widely used across the field of Monte Carlo simulation. Applications include financial risk management, service systems engineering, logistics, and retail operations.
His research centers on computational algorithms, mathematical approximations, statistical methodology, and optimization methods for the analysis of systems in which uncertainty is present. He has developed algorithms that are widely used across the field of Monte Carlo simulation. Applications include financial risk management, service systems engineering, logistics, and retail operations.
Contact
Email
glynn [at] stanford.edu
CV Link
Info Links
External Profile