Presented By: U-M Industrial & Operations Engineering
899 Seminar Series: Lei Ying
Safe Online Learning and Decision-Making
Presenter Bio:
Lei Ying is currently a Professor at the Electrical Engineering and Computer Science Department of the University of Michigan, Ann Arbor, an IEEE Fellow, and an Editor-at-Large for the IEEE/ACM Transactions on Networking. His research is broadly in the interplay of complex
stochastic systems and big data, including reinforcement learning, large-scale communication/computing systems, private data marketplaces, and large-scale graph mining. He won the Young Investigator Award from the Defense Threat Reduction Agency (DTRA) in 2009 and the 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 and the WiOpt’18 Best Student Paper Award; his papers have also been selected in ACM TKDD Special
Issue “Best Papers of KDD 2016”, Fast-Track Review for TNSE at IEEE INFOCOM 2018 (7 out of 312 accepted papers were invited), and Best Paper Finalist at MobiHoc 2019.
Abstract:
Data-driven learning and decision-making in complex systems are often subject to a variety of operational constraints such as safety, fairness, and budget constraints. The problem becomes particularly challenging when the constraints are unknown, sometimes adversarial,
and must be learned while making decisions. This talk presents some of our recent results on this topic, focusing on solving unknown CMDPs using model-free approaches.
Lei Ying is currently a Professor at the Electrical Engineering and Computer Science Department of the University of Michigan, Ann Arbor, an IEEE Fellow, and an Editor-at-Large for the IEEE/ACM Transactions on Networking. His research is broadly in the interplay of complex
stochastic systems and big data, including reinforcement learning, large-scale communication/computing systems, private data marketplaces, and large-scale graph mining. He won the Young Investigator Award from the Defense Threat Reduction Agency (DTRA) in 2009 and the 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 and the WiOpt’18 Best Student Paper Award; his papers have also been selected in ACM TKDD Special
Issue “Best Papers of KDD 2016”, Fast-Track Review for TNSE at IEEE INFOCOM 2018 (7 out of 312 accepted papers were invited), and Best Paper Finalist at MobiHoc 2019.
Abstract:
Data-driven learning and decision-making in complex systems are often subject to a variety of operational constraints such as safety, fairness, and budget constraints. The problem becomes particularly challenging when the constraints are unknown, sometimes adversarial,
and must be learned while making decisions. This talk presents some of our recent results on this topic, focusing on solving unknown CMDPs using model-free approaches.
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