Asu Ozdaglar received the B.S. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1996, and the S.M. and the Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2003, respectively. Since 2003, she has been a member of the faculty of the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology (MIT), where she is currently the Class of 1943 Career Development Associate Professor. She is also a member of the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC) at MIT.
Her research interests include optimization theory (with emphasis on nonlinear programming, convex analysis and nonconvex optimization), game theory, network economics, distributed optimization methods, and network optimization and control. She is the co-author (with Dimitri P. Bertsekas and Angelia Nedic) of the book entitled “Convex Analysis and Optimization” (Athena Scientific, 2003). She is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, and the 2008 Donald P. Eckman award of the American Automatic Control Council.
Most individuals form their opinions about the quality of products, social trends and political issues via their interactions in social and economic networks. While the role of social networks as a conduit for information is as old as humanity, recent social and technological developments, such as Facebook, Blogs and Tweeter, have added further to the complexity of network interactions. Despite the ubiquity of social networks and their importance in communication, we know relatively little about how opinions form and information is transmitted in such networks. For example, does a large social network of individuals holding disperse information aggregate it efficiently? Can falsehoods, misinformation and rumors spread over networks? Do social networks, empowered by our modern communication means, support the wisdom of crowds or their ignorance? Systematic analysis of these questions necessitate a combination of tools and insights from game theory, the study of multiagent systems, and control theory. Game theory is central for studying both the selfish decisions and actions of individuals and the information that they reveal or communicate. Control theory is essential for a holistic study of networks and developing the tools for optimization over networks. In this talk, I report recent work on combining game theoretic and control theoretic approaches to the analysis of social learning over networks.
