| 戴建岗
康奈尔大学教授
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【主讲】康奈尔大学教授戴建岗
【时间】2015年12月23日（周三）13:30-15:30
Jim Dai is a professor in the School of Operations Research and Information Engineering (ORIE) of Cornell University. Prior joining Cornell, he held the Chandler Family Chair of Industrial and Systems Engineering at Georgia Institute of Technology, where he was a faculty member from 1990 to 2012. He is a Special Term Professor at Tsinghua University and a Visiting Professor in Decision Sciences at National University of Singapore. For more than twenty years, he has worked on stochastic models arising from communications, manufacturing, and service systems that include data switches, semiconductor wafer fabrication lines, call centers, and healthcare?delivery systems. Jim Dai is an elected fellow of Institute of Mathematical Statistics and an elected fellow of Institute for Operations Research and the Management Sciences (INFORMS). His awards for research contributions include the Best Publication Award in 1997 and The Erlang Prize in 1998, both from the Applied Probability Society of INFORMS. He delivered the Markov Lecture at INFORMS national meeting in October 2012. He is the Editor-in-Chief for Mathematics of Operations Research, a past Area Editor for Operations Research, and a past Series Editor for Handbooks in Operations Research and Management Science.
Research Interests: Stochastic processing networks, fluid and diffusion models of queuing networks, reflecting brownian motions, stein's method, customer contact center management, hospital inpatient flow management, semiconductor wafer manufacturing, airline revenue management, and orderbook dynamics.
Jim Dai，Professor，Cornell University: Stein's Method and Hospital Inpatient Flow Management
【Speaker】Jim Dai，Professor，Cornell University
【Title】Stein's Method and Hospital Inpatient Flow Management
【Time】Dec.23, 13:30-15:30
【Venue】Room 453, Weilun Building, Tsinghua SEM
【Abstract】Diffusion models have been used for steady-state analysis of many stochastic systems. A recent paper (Braverman and Dai 2015) demonstrates that Stein's method is a natural tool to establish error bounds for steady-state diffusion approximations. It turns out that the method also serves as a practical engineering tool for building robust diffusion models that are accurate in a number of parameter regimes. I will illustrate these advances using a class of new stochastic models capturing patient flows from a hospital emergency department to inpatient wards. This talk is based on a joint work with Pengyi Shi at Purdue University.
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