Provably-good Triangulations for Protein Modeling
Peter Glynn, Department of Management Science and Engineering, Stanford University
October 10, 2003, Cooper Auditorium GSIA

Abstract:

Traditional queueing theory typically assumes that the exogenous traffic flows into the system are stationary stochastic processes. However, most real-world queueing environments exhibit non-stationarities due to time-of-day effects, day-of-week effects, seasonalities, or macro-economic effects. In this talk, we will discuss both modeling issues that arise in trying to capture non-stationarities and associated approximations for non-stationary queues.

Short Biography

Peter Glynn 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 is now the Thomas Ford Professor of Engineering in the Department of Management Science and Engineering. Prof. Glynn also has a courtesy appointment in the Department of Electrical Engineering. He is a Fellow of the Institute of Mathematical Statistics and has research interests in computational probability, queueing theory, statistical inference for stochastic processes, and stochastic modeling.