ISyE Seminar Series: Lei Ying
"Mean Field Approximation, Stein’s Method, and State Space Concentration: A Trident for Understanding Large-Scale Stochastic Systems"
Presentation by Lei Ying
Professor, Electrical Engineering and Computer Science
University of Michigan
3:30 pm - Seminar
4:30 pm - Reception, cookies and coffee
About the seminar:
Large-scale stochastic systems are ubiquitous in practical applications, including foundational models, cloud computing centers, ride-sharing systems, and more. However, the design, control, and analysis of these systems are often challenging due to the curse of dimensionality. In this talk, I will present an analytical framework that combines mean-field approximation, Stein’s method, and state-space concentration, providing a powerful tool for understanding large-scale stochastic systems. This method has been successfully applied to address open questions in various fields such as reinforcement learning, cloud computing, and the Internet of Things. In this talk, I will use the example of load balancing in cloud computing to demonstrate how this approach has enabled us to gain important design insights that would be difficult, if not impossible, to obtain using the traditional mean-field analysis
Lei Ying is a Professor at the Electrical Engineering and Computer Science Department of the University of Michigan, Ann Arbor. His research is broadly in the interplay of complex stochastic systems and big data, including reinforcement learning, large-scale communication/computing systems for big-data processing, private data marketplaces, and large-scale graph mining. He won the Young Investigator Award from the Defense Threat Reduction Agency (DTRA) in 2009 and NSF CAREER Award in 2010. His research contributions have been recognized as best papers in conferences across different disciplines, including communication networks (INFOCOM and WiOpt), computer systems (SIGMETRICS) and data mining (KDD).
Mean field models and Stein's method in general:
Application in cloud computing
Application in reinforcement learning
Application in wireless networks