Spring 2005 UMASS Amherst
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
Williams College
Williamstown, MA

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 1998.

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, wakolbinger@som.umass.edu