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


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, interpretable deep learning, 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, as well as to computer vision. Many of our interpretable machine learning algorithms heavily rely on efficient discrete optimization techniques. Here are some of my major projects:

  • My collaborators and I developed practical code for decision lists and decision trees that provably optimize accuracy and sparsity. (KDD 2017 oral, NeurIPS 2019 spotlight, ICML 2020).

  • Our team's work on optimal scoring systems (sparse linear models with integer coefficients) has been applied to several important healthcare and criminal justice applications. Our work on seizure prediction in ICU patients won the 2019 INFORMS Innovative Applications in Analytics Award. It allowed doctors to monitor 2.8X more patients and saved over $6M at two major hospitals in FY2018. It helps to prevent severe brain damage in critically ill patients.

  • I led a team in the first major effort to maintain an underground electrical distribution network using machine learning, in joint work with Con Edison in NYC. This won the 2013 INFORMS Innovative Applications in Analytics Award.

  • I solved a well-known unsolved theoretical problem in machine learning as a PhD student, which is whether AdaBoost maximizes the margin like SVM. (The answer is that sometimes it provably does and sometimes it provably doesn't.) Subsequent work solved a published COLT open problem, earning a prize.

  • My collaborators and I developed code for detecting crime series in cities. This methodology (specifically, the Series Finder algorithm) was adapted by the NYPD and their application (Patternizr) has been running live in NYC since 2016 for determining whether each new crime is related to past crimes.

  • I have given invited and keynote talks at KDD (2014 and 2019), AISTATS, ECML-PKDD, ML in Healthcare, FAT-ML (Fairness, Accountability, and Transparency), and at several other venues.

  • I enjoy competing in data science competitions and coaching students of teams. We have won awards in several competitions, including the FICO Recognition Award for the first Explainable Machine Learning Competition in 2018, NTIRE Superresolution competition in 2018, and PoeTix Literary Turing Competition in 2018.

  • I am one of three co-PIs of the Almost-Matching-Exactly lab, which develops matching methods for use in interpretable causal inference.


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. She is also an associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). 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 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 gave a Thomas Langford Lecturer at Duke University during the 2019-2020 academic year. She has given keynote/invited talks at several conferences including KDD (twice), AISTATS, CODE, MLHC, FAT-ML, DSAA, and ECML-PKDD.