Cynthia D. Rudin


Professor of Computer Science

Cynthia Rudin is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics & bioinformatics at Duke University, and directs the Interpretable Machine Learning Lab. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is the recipient of the 2021 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI). This award, similar only to world-renowned recognitions, such as the Nobel Prize and the Turing Award, carries a monetary reward at the million-dollar level. She is also a three-time winner of the INFORMS Innovative Applications in Analytics 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. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics.

She is past chair of both the INFORMS Data Mining Section and the Statistical Learning and Data Science Section of the American Statistical Association. She has also served on committees for DARPA, the National Institute of Justice, AAAI, and ACM SIGKDD. She has served on three committees for the National Academies of Sciences, Engineering and Medicine, including the Committee on Applied and Theoretical Statistics, the Committee on Law and Justice, and the Committee on Analytic Research Foundations for the Next-Generation Electric Grid. She has given keynote/invited talks at several conferences including KDD (twice), AISTATS, CODE, Machine Learning in Healthcare (MLHC), Fairness, Accountability and Transparency in Machine Learning (FAT-ML), ECML-PKDD, and the Nobel Conference.

Appointments and Affiliations

  • Professor of Computer Science
  • Professor of Electrical and Computer Engineering
  • Professor of Biostatistics and Bioinformatics
  • Professor of Statistical Science

Contact Information

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


  • 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 474: Data Science Competition
  • COMPSCI 671D: Machine Learning - Introductory PhD Level
  • COMPSCI 891: Special Readings in Computer Science
  • ECE 590D: Advanced Topics in Electrical and Computer Engineering
  • ECE 687D: Theory and Algorithms for Machine Learning
  • ECE 899: Special Readings in Electrical Engineering
  • ME 555: Advanced Topics in Mechanical Engineering
  • STA 493: Research Independent Study
  • STA 671D: Machine Learning - Introductory PhD Level
  • STA 993: Independent Study

In the News

Representative Publications

  • Afnan, MAM; Rudin, C; Conitzer, V; Savulescu, J; Mishra, A; Liu, Y; Afnan, M, Ethical Implementation of Artificial Intelligence to Select Embryos in in Vitro Fertilization, Aies 2021 Proceedings of the 2021 Aaai/Acm Conference on Ai, Ethics, and Society (2021), pp. 316-326 [10.1145/3461702.3462589] [abs].
  • Wang, T; Morucci, M; Awan, MU; Liu, Y; Roy, S; Rudin, C; Volfovsky, A, FLAME: A fast large-scale almost matching exactly approach to causal inference, Journal of Machine Learning Research, vol 22 (2021) [abs].
  • Traca, S; Rudin, C; Yan, W, Regulating greed over time in multi-armed bandits, Journal of Machine Learning Research, vol 22 (2021) [abs].
  • Koyyalagunta, D; Sun, A; Draelos, RL; Rudin, C, Playing codenames with language graphs and word embeddings, Journal of Artificial Intelligence Research, vol 71 (2021), pp. 319-346 [10.1613/jair.1.12665] [abs].
  • Chen, C; Lin, K; Rudin, C; Shaposhnik, Y; Wang, S; Wang, T, A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations, Decision Support Systems (2021) [10.1016/j.dss.2021.113647] [abs].