Cornell’s Jim Dai Delivers 2026 Wasserstrom Lecture
Dai shared research on how to better understand and optimize complex service systems such as data centers and communication networks.
On April 7, Cornell University’s Jim Dai delivered the 2026 Wasserstrom Distinguished Lecture. Attended by faculty and students from Northwestern Engineering and the Kellogg School of Management, the lecture is held annually and is hosted by Northwestern’s Department of Industrial Engineering and Management Sciences.
Dai, the Leon C. Welch Professor of Engineering in the School of Operations Research and Information Engineering at Cornell, gave the talk “M-COF: A Framework for Scalable Control of Stochastic Processing Networks,” sharing new research on how to better understand and optimize complex service systems such as data centers and communication networks.
"It was a distinct honor to welcome Professor Jim Dai to Northwestern for the 2026 Wasserstrom Distinguished Lecture. As a Fellow of the Institute for Operations Research and the Management Sciences (INFORMS) and Institute of Mathematical Statistics, and recent recipient of the INFORMS von Neumann Theory Prize, Jim represents the pinnacle of excellence in our field,” said Simge Küçükyavuz, David A. and Karen Richards Sachs Professor and Chair of Industrial Engineering and Management Sciences. “His pioneering research on stochastic network stability and heavy traffic diffusion approximations continues to shape how we understand complex service systems. Hosting such a preeminent leader provides our community with a unique opportunity to engage with the theoretical foundations that drive real-world innovation in operations research.”
Stochastic processing networks (SPNs) are mathematical models used to describe how these systems handle jobs, tasks, or data over time, and have been studied for more than 40 years. In his talk, Dai introduced M-COF (Multi-scale Closed-form Optimization Framework), a new method for optimizing a class of SPNs known as multi-class queueing networks. M-COF is designed to scale to large systems and can incorporate practical performance considerations such as tail latency and fairness. The framework is grounded in recent advances in multi-scale heavy-traffic theory, which examines system behavior under high demand.
Dai also discussed how M-COF, which does not rely on simulation, compares with newer simulation-based approaches, including methods using deep partial differential equations and deep reinforcement learning.
The Wasserstrom Family Endowment provides support for activities that enhance and supplement the educational experience of IEMS students, including the annual Wasserstrom Family Distinguished Lecture that began in 2012. Alan Wasserstrom is a 1962 graduate of IEMS, the owner and CEO of N. Wasserstrom & Sons, a recipient of the department’s Distinguished Alumnus Award, and a member of the IEMS Advisory Board.