Title: Stein’s Method for Big-Data Systems: From Learning Queues to Q-Learning【IIIS-Haihua Frontier Seminar Series】
Speaker: Lei Ying University of Michigan
Time: 2019-12-30 10:30-2019-12-30 11:30
Venue: FIT 1-222

Abstract:

Big-data analytics involves data collection, data processing, and mining of and learning from data. Stochasticity is ubiquitous in big-data analytics, and often one of the main obstacles in the design, control and analysis of big-data systems. In the first part of this talk, I will review some open problems in private-data market design, cloud computing and reinforcement learning related to stochastic analysis. I will then introduce an analytical framework, inspired by Stein’s method in probability theory, for analyzing stochastic big-data systems. I will present two previously open problems which we solved recently: (i) how much information is needed to balance the load in cloud computing systems to achieve asymptotically zero queueing delay? (ii) how many samples are needed in reinforcement learning to learn value functions or Q-functions with function approximation?



Short Bio:

Lei Ying received his B.E. degree from Tsinghua University, Beijing, China, and his M.S. and Ph.D in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. He currently is a Professor at the Electrical Engineering and Computer Science Department of the University of Michigan, Ann Arbor, and an Associate Editor of the IEEE Transactions on Information Theory. His research is broadly in the interplay of complex stochastic systems and big-data, including large-scale communication/computing systems for big-data processing, private data marketplaces, and large-scale graph mining. He coauthored books Communication Networks: An Optimization, Control and Stochastic Networks Perspective, Cambridge University Press, 2014; and Diffusion Source Localization in Large Networks, Synthesis Lectures on Communication Networks, Morgan & Claypool Publishers, 2018.  He won the Young Investigator Award from the Defense Threat Reduction Agency (DTRA) in 2009 and NSF CAREER Award in 2010. my research contributions have been recognized as best papers in conferences across different disciplines, including communication networks (INFOCOM andWiOpt), computer systems (SIGMETRICS) and data mining (KDD).