Department of Industrial Engineering & Decision Analytics [IEDA Seminar] - Sixty Years of Applied Probability

10:30am - 11:30am
Room 2405 (lift 17-18)

About 60 years ago, the term "applied probability" (AP) became official when the Journal of Applied Probability was founded at the University of Sheffield. In the years that followed, both the Operations Research Society of America and the Institute for Mathematical Statistics formally designated AP as a subfield of their respective disciplines. By coincidence, my first exposure to an AP model (specifically, the M/M/1 queue) also came about 60 years ago, as an undergraduate engineering student. In this lecture I will look back on developments in the field, in some which I have participated, with particular emphasis on (a) how to define and characterize AP, and (b) shining jewels of the AP literature.

Event Format
Speakers / Performers:
Prof. J. Michael HARRlSON
School of Business, Stanford University

J. MICHAEL HARRlSON is the Adams Distinguished Professor of Management, Emeritus, in the Graduate School of Business, Stanford University.  He earned a B.S. degree in industrial engineering from Lehigh University, an M.S. in industrial engineering from Stanford, and a Ph.D. in operations research from Stanford before joining the faculty of the Graduate School of Business in 1970.  He has developed and analyzed stochastic models in several different domains related to business, including mathematical finance, processing network theory, call center scheduling, dynamic pricing and revenue management. Professor Harrison has been honored by INFORMS with its Expository Writing Award (1998), the Lanchester Prize for best research publication (2001), and the John von Neumann Theory Prize (2004); he was elected to the National Academy of Engineering in 2008.  He is a fellow of INFORMS and of the Institute for Mathematical Statistics.

Recommended For
Faculty and staff
PG students
Department of Industrial Engineering & Decision Analytics
Department of Information Systems, Business Statistics & Operations Management
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