CS Faculty Photo

Professor of Computer Science, Professor of Electrical and Computer Engineering, Professor of Mathematics, Professor of Statistical Science

Faculty Area:
Machine learning, artificial intelligence, and algorithms.
cynthia at cs.duke.edu
(919) 660-6555
Web page:

Ph.D., Princeton University
B.S./B.A., University of New York at Buffalo (SUNY), Honors Program

Honors & Awards



My research focuses on machine learning tools that help humans make better decisions, mainly interpretable machine learning.

Selected Publications
  • Optimal Sparse Decision Trees. NeurIPS spotlight (top 3% of papers), 2019. Xiyang Hu, Cynthia Rudin, and Margo Seltzer
  • This Looks Like That: Deep Learning for Interpretable Image Recognition. NeurIPS spotlight (top 3% of papers), 2019. Chaofan Chen, Oscar Li, Alina Barnett, Jonathan Su, Cynthia Rudin
  • Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and use Interpretable Models Instead, Nature Machine Intelligence, 2019. Cynthia Rudin
  • Learning Optimized Risk Scores. JMLR, 2019. Shorter version at KDD 2017. Berk Ustun and Cynthia Rudin
  • Learning Certifiably Optimal Rule Lists for Categorical Data. Journal of Machine Learning Research, 2018. Shorter version published in KDD 2017 (oral). Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin
Extended List of Publications

Publications by Cynthia Rudin