Lecture: Learning and Exploiting Statistical Dependencies in Networks
Professor David Jensen, of the UMass Department of Computer Science will deliver this lecture, titled Learning and Exploiting Statistical Dependencies in Networks, as part of the Fall 2007 Operations Research/Management Science Seminar series. All are invited to attend.
Abstract: Networks are a ubiquitous representation for natural, technological, and social systems. We live embedded in social and professional networks, we communicate through telecommunications and computer networks, and we represent information in documents connected by hyperlinks and bibliographic citations. Only recently, however, have researchers developed techniques to analyze and model data probabilistic dependencies in these networks. These techniques build on work in artificial intelligence, statistics, databases, graph theory, and social network analysis, and they are profoundly expanding the phenomena that we can understand and predict. However, new frontiers await.
In this talk, Jensen will survey some recent work in learning probabilistic models of relational data, and discuss several applications of these techniques, including fraud detection in the U.S. securities industry. He will argue that current techniques are capable of learning only a subset of the knowledge needed by practitioners in these domains, and that further work in analyzing networks offers a unique ability to produce the full range of knowledge needed in a wide range of applications, including a unification of work in machine learning, causal inference, and agent-based simulation.
More information about Prof. David Jensen.
This series is organized by the UMass Amherst INFORMS Student Chapter. Support for this series is provided by the Isenberg School of Management, the Department of Finance and Operations Management, and the John F. Smith Memorial Fund.
