Cynthia D. Rudin, Associate Professor of Computer Science and Electrical and Computer Engineering and Statistical Science and Mathematics
Cynthia Rudin is an associate professor of computer science, electrical and computer engineering, statistical science and mathematics at Duke University, and directs the Prediction Analysis Lab. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and 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, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. Work from her lab has won 10 best paper awards in the last 5 years. She is past chair of the INFORMS Data Mining Section, and is currently chair of the Statistical Learning and Data Science section of the American Statistical Association. She also serves on (or has served on) committees for DARPA, the National Institute of Justice, the National Academy of Sciences (for both statistics and criminology/law), and AAAI.  Contact Info:
Office Location:   Office Phone:  (919) 6606581  Email Address:    Education:
Ph.D.  Princeton University  2004 
 Recent Publications
(More Publications)
 Vu, MAT; Adalı, T; Ba, D; Buzsáki, G; Carlson, D; Heller, K; Liston, C; Rudin, C; Sohal, VS; Widge, AS; Mayberg, HS; Sapiro, G; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience.,
The Journal of neuroscience : the official journal of the Society for Neuroscience, vol. 38 no. 7
(February, 2018),
pp. 16011607 [doi] [abs]
 Struck, AF; Ustun, B; Ruiz, AR; Lee, JW; LaRoche, SM; Hirsch, LJ; Gilmore, EJ; Vlachy, J; Haider, HA; Rudin, C; Westover, MB, Association of an ElectroencephalographyBased Risk Score With Seizure Probability in Hospitalized Patients.,
JAMA Neurology, vol. 74 no. 12
(December, 2017),
pp. 14191424 [doi] [abs]
 Ustun, B; Rudin, C, Optimized risk scores,
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685
(August, 2017),
pp. 11251134, ISBN 9781450348874 [doi] [abs]
 Angelino, E; LarusStone, N; Alabi, D; Seltzer, M; Rudin, C, Learning certifiably optimal rule lists,
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685
(August, 2017),
pp. 3544, ISBN 9781450348874 [doi] [abs]
 Wang, T; Rudin, C; DoshiVelez, F; Liu, Y; Klampfl, E; MacNeille, P, A Bayesian framework for learning rule sets for interpretable classification,
Journal of machine learning research : JMLR, vol. 18
(August, 2017),
pp. 137 [abs]
 Recent Grant Support
 QuBBD: Collaborative Research: Matching Methods for causal inference: big data and networks, National Institutes of Health, 1R01EB02502101, 2017/092020/06.
