You are here

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

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 393: Research Independent Study
  • COMPSCI 394: Research Independent Study
  • COMPSCI 571D: Machine Learning
  • ECE 682D: Probabilistic Machine Learning
  • ECE 899: Special Readings in Electrical Engineering
  • STA 493: Research Independent Study
  • STA 561D: Probabilistic Machine Learning
  • STA 993: Independent Study

In the News

Representative Publications

  • Rudin, C; Ertekin, Ş, Learning customized and optimized lists of rules with mathematical programming, Mathematical Programming Computation, vol 10 no. 4 (2018), pp. 659-702 [10.1007/s12532-018-0143-8] [abs].
  • Rudin, C; Ustunb, B, Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice, Interfaces, vol 48 no. 5 (2018), pp. 449-466 [10.1287/inte.2018.0957] [abs].
  • Vu, M-AT; 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 (2018), pp. 1601-1607 [10.1523/jneurosci.0508-17.2018] [abs].
  • Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C, Learning certifiably optimal rule lists for categorical data, Journal of Machine Learning Research, vol 18 (2018), pp. 1-78 [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 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].