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

Terng Lecturer, Institute for Advanced Study, Princeton 2020 (upcoming)
Thomas Langford Lecture Award, Duke University, 2019-2020
2019 INFORMS Innovative Applications in Analytics Award
Fellow of the American Statistical Association, 2019-
Fellow of the Institute of Mathematical Statistics, 2019-
Runner up, Invenia Labs SEE Award 2018 - Supporting Machine Learning Research with a Positive Impact on Social, Economic, or Environmental (SEE) Challenges
Finalist for INFORMS 2017 Daniel H. Wagner Prize for Excellence in Operations Research
Winner of the FICO Recognition Award for the Explainable Machine Learning Challenge, 2018
2016 INFORMS Innovative Applications in Analytics Award
2013 INFORMS Innovative Applications in Analytics Award
Student paper awards for Rudin’s lab are from: the American Statistical Association's Statistical Learning and Data Science Section, INFORMS Computing Society, INFORMS Data
Mining Section, INFORMS QSR Section, PoetiX Literary Turing Test, NTIRE-CVPR Image Super-Resolution Challenge, IBM Service Science Best Paper (finalist), INFORMS Data
Mining & Decision Analytics (DMDA) Workshop, INFORMS Doing Good with Good OR Paper Competition


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