Description
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? 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 School of Electrical, Computer and Energy Engineering at Arizona State University, 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. He was the Northrop Grumman Assistant Professor in the Department of Electrical and Computer Engineering at Iowa State University from 2010 to 2012. His papers have received the best paper award at IEEE INFOCOM 2015, the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS/IFIP Performance 2016, been selected in ACM TKDD Speical Issue "Best Papers of KDD 2016", received the WiOpt'18 Best Student Paper Award, and selected for Fast-Track Review for TNSE at IEEE INFOCOM 2018 (7 out of 312 accepted papers were invited).