Fall 2007 UMASS Amherst
Operations Research / Management Science Seminar Series

Date: Friday, October 26, 2007

Time: 11:00 AM
Location: Isenberg School of Management, Room 112

Speaker: Professor David Jensen

Department of Computer Science
University of Massachusetts at Amherst

Biography: David Jensen is Associate Professor of Computer Science and Director of the Knowledge Discovery Laboratory at the University of Massachusetts Amherst.  He received his doctorate from Washington University in 1992.  From 1991 to 1995, he served as an analyst with the Office of Technology Assessment, an agency of the United States Congress.  His research focuses on machine learning and knowledge discovery in relational data, with applications to social network analysis, web mining, and fraud detection.  He serves on the program committees of the International Conference on Knowledge Discovery and Data Mining and the International Conference on Machine Learning.  He is a member of the 2006-2007 Defense Science Study Group.

TITLE: Learning and Exploiting Statistical Dependencies in Networks
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, I 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.  I 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.
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, INFORMS, and the John F. Smith Memorial Fund.

For questions, please contact the INFORMS Student Chapter Speaker Series Coordinator, Ms. Trisha Woolley, twoolley@som.umass.edu
or the Faculty Advisor, Professor Anna Nagurney.