CS-EE Seminar: Online Learning in Big Data
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  • CS-EE Seminar: Online Learning in Big Data

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Speaker: Cem Tekin
Title: Online Learning in Big Data
Time: 13:40 -- 14:30
Place: FENS L067

Abstract:  Recommender systems, medical diagnosis, network security, etc., require on-going learning and decision-making in real time. These – and many others – represent perfect examples of the opportunities and difficulties presented by Big Data: the available information often arrives from a variety of sources and has diverse features so that learning from all the sources may be valuable but integrating what is learned is subject to the curse of dimensionality. In this talk, I will present recent pieces of work addressing these challenges. The first is about learning and exploiting the information relevant to each action, when the information available at each decision step is high dimensional but the most relevant information is embedded into only a few relevant dimensions. If these relevant dimensions were known in advance, the problem would be simple – but they are not. Moreover, the relevant dimensions may be different for different actions. The second concerns promoting cooperation among informationally decentralized learners that learn how to act on heterogeneous streams of information. Examples of the first two works include recommender systems, expert selection for medical diagnosis and multimedia content aggregation. The third work concerns decision problems in which the consequences (rewards) of different actions are correlated, so that selecting one action provides information about the consequences (rewards) of other actions as well. Examples of the third work range from drug dosage to dynamic pricing.

Bio: Cem Tekin is an Assistant Professor in Electrical and Electronics Engineering Department, Bilkent University. He received his PhD degree in Electrical Engineering: Systems from the University of Michigan in 2013. He also received his MS degree in Mathematics and MSE degree in Electrical Engineering: Systems, from the University of Michigan in 2011 and 2010, respectively. Prior to attending the University of Michigan, He received his BS in Electrical and Electronics Engineering (valedictorian) from METU in 2008. From February 2013 to January 2015 he was a postdoctoral scholar in Electrical Engineering Department, UCLA. He received the University of Michigan Electrical Engineering Departmental Fellowship in 2008, and the Fred W. Ellersick award for the best paper in MILCOM 2009. Dr. Tekin has authored or coauthored over 35 research papers, 2 book chapters and a research monograph. His research spans the area of machine learning, data science, online learning and multi-armed bandit problems.