Cynthia Rudin

 

Associate Professor of Computer Science and Electrical and Computer Engineering
PI, Prediction Analysis Lab
Duke University

Office: LSRC D224
Research Drive
Durham NC, USA

Bio:

Cynthia Rudin is an associate professor of computer science and electrical and computer engineering at Duke University, with secondary appointments in the statistics and mathematics departments. She directs the Prediction Analysis Lab. Her interests are in machine learning, data mining, applied statistics, and knowledge discovery (Big Data), and in particular, machine learning models that are interpretable to human experts. Her application areas are in energy grid reliability, healthcare, and computational criminology. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo where she received the College of Arts and Sciences Outstanding Senior Award in Sciences and Mathematics, and three separate outstanding senior awards from the departments of physics, music, and mathematics. She received a PhD in applied and computational mathematics from Princeton University. She is the recipient of the 2013 and 2016 INFORMS Innovative Applications in Analytics Awards, an NSF CAREER award, was named as one of the “Top 40 Under 40” by Poets and Quants in 2015, was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015, and won an Adobe Digital Marketing Research Award in 2016. Her work has been featured in Businessweek, The Wall Street Journal, the New York Times, the Boston Globe, the Times of London, Fox News (Fox & Friends), the Toronto Star, WIRED Science, U.S. News and World Report, Slashdot, CIO magazine, Boston Public Radio, and on the cover of IEEE Computer. She serves on committees for DARPA, the American Statistical Association, INFORMS, the National Institute of Justice, and the National Academy of Sciences. She is past-chair of the INFORMS Data Mining Section, and is chair-elect of the Statistical Learning and Data Science section of the American Statistical Association.