|Spring 2005 UMASS
Operations Research / Management Science Seminar Series
Date: Friday, January 28, 2005
Time: 11:00 AM
Location: Isenberg School of Management, Room 112
|Speaker: Professor Richard DeVeaux
Department of Mathematics and Statistics
|Biography: Dick De Veaux holds degrees in Civil
Engineering (B.S.E. Princeton), Mathematics (A.B. Princeton), Dance
Education (M.A. Stanford) and Statistics (Ph.D., Stanford). He has
taught at the Wharton School, the Princeton University School of
Engineering, and, since 1994, has been a professor of Statistics in the
Math and Stat Department of Williams College. He has won numerous
teaching awards including a “Lifetime Award for Dedication and
Excellence in Teaching” from the Engineering Council at
Princeton. He has won both the Wilcoxon and Shewell awards from
the American Society for Quality and was elected a fellow of the ASA in
Dick has been a consultant for over 20 years for such Fortune 500 companies as Hewlett-Packard, Alcoa, Bank One, GlaxoSmithKline, Dupont, Pillsbury, Rohm and Haas, Ernst and Young, and General Electric. He holds two U.S. patents and is the author of over 30 refereed journal articles. He is the co-author, with Paul Velleman and David Bock, of the critically acclaimed textbooks Intro Stats, Stats: Modeling the World, and Stats: Data and Models, all published by Addison-Wesley.
His hobbies include cycling, swimming, singing (barbershop, doo wop and classical -- he is the head of the Diminished Faculty, a local doo wop group) -- and dancing (he was once a professional dancer and teaches Modern Dance during Winter Study at Williams).
|TITLE: Predictive Analytics:
Modeling the World
|Abstract: The sheer volume and complexity
of data collected or available to most organizations has created an
imposing barrier to its effective use. These challenges have propelled
data mining to the forefront of making profitable and effective use of
data. Data mining is a process that uses a variety of data analysis and
modeling techniques to discover patterns and relationships in data that
may be used to make accurate predictions. While the most widespread
application of data mining are in CRM (customer relationship
management) some of the other important applications include fraud
detection and identifying good credit risks.
But data description alone cannot provide an action plan. You must first build a predictive model based on patterns determined from known results, then test that model on results outside the original sample. In classical data analysis, the exploratory phase usually precedes the model selection phase. It’s seen as a necessary preliminary for understanding the data before beginning to think about how to model it. But in data mining, sometimes we start with a preliminary model just to narrow down the set of potential predictors. This exploratory data modeling (EDM) seems to be at odds with standard statistical practice, but, in fact, it’s simply using models as a new exploratory tool.
In this talk, we’ll take a brief tour of the current state of data mining algorithms and using several case studies to explain how EDM can be used to narrow the search for a predictive model and how data mining can add value by producing useful and meaningful results.
|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.
For questions, please contact the INFORMS Student Chapter President, Ms. Tina Wakolbinger, email@example.com