| 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 |