Cynthia D. Rudin

Cynthia D. Rudin

Associate Professor of Computer Science

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 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.

Appointments and Affiliations

  • Associate Professor of Computer Science
  • Associate Professor of Electrical and Computer Engineering
  • Associate Professor of Mathematics
  • Associate Professor of Statistical Science


  • Ph.D. Princeton University, 2004

Courses Taught

  • COMPSCI 290: Topics in Computer Science
  • COMPSCI 394: Research Independent Study
  • COMPSCI 571D: Machine Learning
  • ECE 682D: Probabilistic Machine Learning
  • ECE 899: Special Readings in Electrical Engineering
  • STA 561D: Probabilistic Machine Learning
  • STA 993: Independent Study

In the News

Representative Publications

  • 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 Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients., JAMA Neurology, vol 74 no. 12 (2017), pp. 1419-1424 [10.1001/jamaneurol.2017.2459] [abs].
  • Ustun, B; Rudin, C, Optimized risk scores, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol Part F129685 (2017), pp. 1125-1134 [10.1145/3097983.3098161] [abs].
  • Angelino, E; Larus-Stone, 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 (2017), pp. 35-44 [10.1145/3097983.3098047] [abs].
  • Wang, T; Rudin, C; Doshi-Velez, 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 (2017), pp. 1-37 [abs].
  • Letham, B; Letham, PA; Rudin, C; Browne, EP, Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]., Chaos, vol 27 no. 6 (2017) [10.1063/1.4986799] [abs].