Cynthia Rudin is an Associate Professor of statistics at MIT associated with the Computer Science and Artificial Intelligence Laboratory and the Sloan School of Management. She also directs the Prediction Analysis Lab.
Rudin's interests are in machine learning, data mining, applied statistics, and knowledge discovery (Big Data). Her application areas are in energy grid reliability, healthcare, and computational criminology. Previously, Rudin was an associate research scientist at the Center for Computational Learning Systems at Columbia University, and prior to that, an NSF postdoctoral research fellow at NYU.
She is the recipient of the 2013 INFORMS Innovative Applications in Analytics Award, an NSF CAREER award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. 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 is presently the chair of the INFORMS Data Mining Section, and currently serves on committees for DARPA, the National Academy of Sciences, the US Department of Justice, and the American Statistical Association.
Rudin 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.
General Expertise: Algorithms; Algorithms; Algorithms; Analytics; Applied math; Artificial intelligence; Artificial intelligence; Bayesian statistics; Big data; Business intelligence; Business intelligence; Data analysis; Data analytics; Data mining; Data mining; Data mining; Data mining; Decision making; Decision support; Electricity; Machine learning; Medical decision making; Predictive analytics; Predictive analytics; Probability; Sports analytics; Statistics; Statistics
For more background on this faculty member's research and academic initiatives, please visit the MIT Sloan faculty directory.