Cynthia Rudin


Associate Professor of Computer Science and Electrical and Computer Engineering
PI, Prediction Analysis Lab
Duke University

Office: LSRC D342
Research Drive
Durham NC, 27708 USA


My research focuses on machine learning tools that help humans make better decisions. This includes the design of algorithms for interpretable machine learning, interpretable policy design, variable importance measures, causal inference methods, new forms of decision theory, ranking methods that assist with prioritization, uncertainty quantification, and methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning. These techniques are applied to critical societal problems in criminology, healthcare, and energy grid reliability. The interpretable machine learning algorithms heavily rely on efficient discrete optimization techniques and Bayesian hierarchical modeling.


Cynthia Rudin is an associate professor of computer science, electrical and computer engineering, and statistics 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 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.