Cynthia Rudin

Past Awards

Student Paper Award: Finalist
Winning material: Exploring the Whole Rashomon Set of Sparse Decision Trees

Best OM Paper in Operations Research Award: Winner(s)

Innovative Applications in Analytics Award: Winner(s)
Winning material: Transparent Machine Learning Models for Predicting Seizures in ICU Patients from cEEG Signals

Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research: Finalist

Innovative Applications in Analytics Award: Winner(s)
Winning material: An Analytics Approach to the Clock Drawing Test for Cognitive Impairment

Innovative Applications in Analytics Award: Winner(s)
Winning material: Machine Learning for Power Grid Reliability: Predicting Manhole Events in New York City
2013 - Winner(s)

Cynthia Rudin, Massachusetts Institute of Technology;
Seyda Ertekin, Massachusetts Institute of Technology;
Rebecca Passonneau, Axinia Radeva, Ashish Tomar, Boyi Xie, Columbia University;
Stanley Lewis, Mark Riddle, Debbie Pangsrivinij, John Shipman, Steve Ierome and Delfina Isaac, Con Edison Company of New York


We summarize the first major effort to use analytics for preemptive maintenance and repair of an electrical distribution network. This is a large-scale multi-year effort between scientists and students at Columbia and MIT and engineers from Con Edison, which operates the world's oldest and largest underground electrical system. Con Edison's preemptive maintenance programs are less than a decade old, and are made more effective with the use of analytics developing alongside the maintenance programs themselves. Some of the data used for our projects are historical records dating as far back as the 1880's, and some of the data are free text documents typed by dispatchers. The operational goals of this work are to assist with Con Edison's preemptive inspection and repair program, and its vented cover replacement program. This has acontinuing impact on public safety, operating costs, and reliability of electrical service in New York City.