Jim Dai
ORIE, Cornell University
Stochastic processing networks (SPNs) model the operations of many complex systems, such as data centers and communication networks. These networks have been an active subject of research for more than 40 years. In this talk, I will introduce M-COF (Multi-scale Closed-form Optimization Framework), a new framework for the optimal control of a class of SPNs known as multi-class queueing networks. M-COF is scalable, can incorporate practical performance metrics such as tail-latency or fairness, and is rooted in recently developed multi-scale heavy-traffic theory. I will also discuss the relationship between M-COF (simulation-free) and recent methods (with simulation) based on deep PDEs and deep reinforcement learning. This talk is based on joint work with Jin Guang (Chicago Booth) and Lucy Huo (HKUST).
Bio: Jim Dai is the Leon C. Welch Professor of Engineering in the School of Operations Research and Information Engineering at Cornell University. Prior to joining Cornell, he held the Chandler Family Chair Professorship in the School of Industrial and Systems Engineering at the Georgia Institute of Technology, where he was a faculty member from 1990 to 2012.
Jim Dai received his BA and MA in mathematics from Nanjing University and his PhD in mathematics from Stanford University. He is an elected Fellow of the Institute of Mathematical Statistics and an elected Fellow of the Institute for Operations Research and the Management Sciences (INFORMS). His awards include the Erlang Prize (1998), the ACM SIGMETRICS Achievement Award (2018), and the INFORMS von Neumann Theory Prize (2024). He served as Editor-in-Chief of Mathematics of Operations Research from 2012 to 2019.
Jim Dai’s research interests include fluid and diffusion models of queueing networks, Stein’s method, stochastic processing networks and their applications to ride-hailing platforms, data centers, and hospital inpatient flow management.
Kendall Minta
(847) 491-8976
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