Cynthia D. Rudin
Associate Professor of Statistical 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 Businessinsider.com 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.
Rudin, C., and Y. Wang. “Direct learning to rank and rerank.” International Conference on Artificial Intelligence and Statistics, Aistats 2018, 2018, pp. 775–83.
Chen, C., and C. Rudin. “An optimization approach to learning falling rule lists.” International Conference on Artificial Intelligence and Statistics, Aistats 2018, 2018, pp. 604–12.
Lakkaraju, H., and C. Rudin. “Learning cost-effective and interpretable treatment regimes.” Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Aistats 2017, 2017.
Garg, V. K., et al. “CRAFT: ClusteR-specific Assorted Feature selecTion.” Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 2016, pp. 305–13.