Cynthia Rudin

 

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

Office: LSRC D342
Research Drive
Durham NC, 27708 USA

Research:

My research focuses on machine learning tools that help humans make better decisions, mainly interpretable machine learning. This also includes variable importance measures, causal inference methods, new forms of decision theory, 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.

Bio:

Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University, and directs the Prediction Analysis Lab, whose main focus is in interpretable machine learning. 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 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 Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. 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, and AAAI. 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 is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. She will be the Thomas Langford Lecturer at Duke University during the 2019-2020 academic year.