Fall 2006 UMASS Amherst
Operations Research / Management Science Seminar Series

Date: Friday, November 3, 2006

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

Speaker: Professor Andrew McCallum

Department of Computer Science
University of Massachusetts at Amherst

Biography: Andrew McCallum is an Associate Professor in the Computer Science Department at University of Massachusetts Amherst.  He was previously Vice President of Research and Development at WhizBang Labs, a company that used machine learning for information extraction from the Web. In the late 1990's he was a Research Scientist and Coordinator at Justsystem Pittsburgh Research Center, where he spearheaded the creation of CORA, an early research paper search engine that used machine learning for spidering, extraction, classification and citation analysis.  McCallum received his PhD from the University of Rochester in 1995, followed by a post-doctoral fellowship at Carnegie Mellon University.  He is currently an action editor for the Journal of Machine Learning Research, and on the board of the International Machine Learning Society.  For the past ten years, McCallum has been active in research on statistical machine learning applied to text, especially information extraction, document classification, clustering, finite state models, semi-supervised learning, and social network analysis.  New work on search and bibliometric analysis of open-access research literature can be found at http://rexa.info. McCallum's web page: http://www.cs.umass.edu/~mccallum.

TITLE: Bayesian Models of Social Networks and Text
Abstract: The field of social network analysis studies mathematical models of patterns in the interactions between people or other entities.  In this talk I will present several recent advances in generative, probabilistic modeling of networks and their per-edge attributes.  The Author-Recipient-Topic model discovers role-similarity between entities by examining not only network connectivity, but also the words communicated on on those edges; I'll demonstrate this method on a large corpus of email data subpoenaed as part of the Enron investigation.  The Group-Topic model discovers groups of entities and the "topical" conditions under which different groupings arise; I'll demonstrate this on coalition discovery from many years worth of voting records in the U.S. Senate and the U.N.  I'll conclude with further examples of Bayesian networks successfully applied to relational data, as well as discussion of their applicability to trend analysis, expert-finding and bibliometrics.

Joint work with colleagues at UMass and Google: Xuerui Wang, Natasha  Mohanty, and Andres Corrada.

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