Supervised Ranking for Manhole Event Mitigation

by Cynthia Rudin

Columbia University

Date

Thursday, March 5, 2009

Time

3:00 p.m. — 4:00 p.m.

Place

Persimmon Room, IMU

Abstract: I will describe work on the Columbia/Con Edison Manhole Events project. An important goal of the project is to produce a ranked list of manholes and service boxes in Manhattan, in order of vulnerability to serious events such as fires, explosions, and smoking manholes.  This list will assist Con Edison with prioritization of repair work.  Of over 50,000 manholes and service boxes, only 0.1 to 1 percent are implicated in a given event each year, so the top of the ranked list needs to be very accurate. Several sources of Con Edison data are used for this task, the most important of which is the ECS (Emergency Control Systems) database, consisting of historical trouble tickets from past events that are mainly recorded in free text by Con Edison dispatchers.  For the ranking task, I will describe a supervised ranking algorithm that concentrates at the top of a ranked list, called the "P-Norm Push." The problem of supervised ranking is to order a set of objects based on a sample of labeled preference data. Such problems arise not only for the manhole event prediction problem, but also for many other industrial prioritization problems, information retrieval tasks such as document retrieval, and other applications in natural language processing such as name tagging. In many of these applications, the ranking accuracy at the top of the list is more important than farther down the list.  I will introduce the problem of manhole event prediction and derive the P-Norm Push algorithm along with some theoretical properties.  Results on a blind prediction test indicate the usefulness of this approach for mitigation of future manhole events in Manhattan.  Papers relating to this talk can be found at:  http://www1.ccls.columbia.edu/~rudin/main.html, listed under "Manhole Events" and "P-Norm Ranking." This work is in collaboration with Rebecca Passonneau, Axinia Radeva, Robert Schapire, Ingrid Daubechies, Heng Ji, Ralph Grishman, and several others.

Biography:  Cynthia Rudin is an associate research scientist at the Center for Computational Learning Systems at Columbia University. She received her Bachelor of Science degree in mathematical physics along with a Bachelors of Arts in music theory at the State University of New York at Buffalo in 1999. She completed her Ph.D. in Applied and Computational Mathematics at Princeton University in 2004, under the supervision of Ingrid Daubechies and Robert E. Schapire. From 2004-2007, she worked as an NSF postdoctoral research fellow at New York University before joining the Center for Computational Learning Systems at Columbia in March of 2007.

Colloquium Provided By:

the School of Informatics