Cynthia D. Rudin


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

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

Contact Information

  • Office Location: LSRC D342, Durham, NC 27708
  • Office Phone: (919) 660-6555


  • Ph.D. Princeton University, 2004

Research Interests

Machine learning, interpretability and transparency of predictive models, causal inference, energy, criminal justice, healthcare

Courses Taught

  • COMPSCI 290: Topics in Computer Science
  • COMPSCI 391: Independent Study
  • COMPSCI 393: Research Independent Study
  • COMPSCI 394: Research Independent Study
  • COMPSCI 571D: Machine Learning
  • COMPSCI 671D: Machine Learning - Introductory PhD Level
  • COMPSCI 891: Special Readings in Computer Science
  • ECE 590D: Advanced Topics in Electrical and Computer Engineering
  • ECE 682D: Probabilistic Machine Learning
  • ECE 687D: Machine Learning - Introductory PhD Level
  • STA 493: Research Independent Study
  • STA 561D: Probabilistic Machine Learning
  • STA 671D: Machine Learning - Introductory PhD Level
  • STA 993: Independent Study

In the News

Representative Publications

  • Fisher, A; Rudin, C; Dominici, F, All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously, Journal of Machine Learning Research, vol 20 (2019) [abs].
  • Wang, F; Rudin, C; Mccormick, TH; Gore, JL, Modeling recovery curves with application to prostatectomy., Biostatistics (Oxford, England), vol 20 no. 4 (2019), pp. 549-564 [10.1093/biostatistics/kxy002] [abs].
  • Rudin, C, Do Simpler Models Exist and How Can We Find Them?, Proceedings of the 25th Acm Sigkdd International Conference on Knowledge Discovery & Data Mining (2019) [10.1145/3292500.3330823] [abs].
  • Ustun, B; Rudin, C, Learning optimized risk scores, Journal of Machine Learning Research, vol 20 (2019) [abs].
  • Rudin, C; Shaposhnik, Y, Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation (2019) [abs].