Cynthia D. Rudin

Earl D. McLean, Jr. Professor

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 2022 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. Her work has been featured in news outlets including the NY Times, Washington Post, Wall Street Journal, the Boston Globe, Businessweek, and NPR.

Appointments and Affiliations

  • Earl D. McLean, Jr. Professor
  • Professor of Computer Science
  • Professor of Electrical and Computer Engineering
  • Professor of Biostatistics and Bioinformatics
  • Professor of Statistical Science

Contact Information

  • Office Location: LSRC D207, Durham, NC 27708
  • Office Phone: +1 919 660 6555
  • Websites:


  • Ph.D. Princeton University, 2004

Courses Taught

  • STA 693: Research Independent Study
  • STA 671D: Theory and Algorithms for Machine Learning
  • STA 493: Research Independent Study
  • STA 393: Research Independent Study
  • ME 555: Advanced Topics in Mechanical Engineering
  • MATH 494: Research Independent Study
  • MATH 491: Independent Study
  • ISS 796T: Bass Connections Information, Society & Culture Research Team
  • ISS 396T: Bass Connections Information, Society & Culture Research Team
  • ECE 687D: Theory and Algorithms for Machine Learning
  • ECE 392: Projects in Electrical and Computer Engineering
  • COMPSCI 671D: Theory and Algorithms for Machine Learning
  • COMPSCI 474: Data Science Competition
  • COMPSCI 394: Research Independent Study
  • COMPSCI 393: Research Independent Study
  • COMPSCI 391: Independent Study

In the News

Representative Publications

  • Falcinelli, Shane D., Alicia D. Cooper-Volkheimer, Lesia Semenova, Ethan Wu, Alexander Richardson, Manickam Ashokkumar, David M. Margolis, et al. “Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People With HIV on Suppressive Antiretroviral Therapy.” J Infect Dis 228, no. 11 (November 28, 2023): 1600–1609.
  • Garrett, Brandon L., and Cynthia Rudin. “Interpretable algorithmic forensics.” Proceedings of the National Academy of Sciences of the United States of America 120, no. 41 (October 2023): e2301842120.
  • Hahn, S., R. Zhu, S. Mak, C. Rudin, and Y. Jiang. “An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation.” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 4089–99, 2023.
  • McDonald, Samantha M., Emily K. Augustine, Quinn Lanners, Cynthia Rudin, L. Catherine Brinson, and Matthew L. Becker. “Applied machine learning as a driver for polymeric biomaterials design.” Nature Communications 14, no. 1 (August 2023): 4838.
  • Peloquin, J., A. Kirillova, C. Rudin, L. C. Brinson, and K. Gall. “Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning.” Materials and Design 232 (August 1, 2023).