# Cynthia D. Rudin

- Associate Professor of Computer Science
- Associate Professor of Electrical and Computer Engineering (Joint)
- Associate Professor of Statistical Science (Secondary)
- Associate Professor of Mathematics (Secondary)

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. *Turning prediction tools into decision tools*. Vol. 9356, 2015.

Bravo, Fernanda, et al. *Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs*. May 2019.

Ban, G. Y., and C. Rudin. “The big Data newsvendor: Practical insights from machine learning.” *Operations Research*, vol. 67, no. 1, Jan. 2019, pp. 90–108. *Scopus*, doi:10.1287/opre.2018.1757.
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Rudin, C., and Ş. Ertekin. “Learning customized and optimized lists of rules with mathematical programming.” *Mathematical Programming Computation*, vol. 10, no. 4, Dec. 2018, pp. 659–702. *Scopus*, doi:10.1007/s12532-018-0143-8.
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Rudin, C., and B. Ustunb. “Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice.” *Interfaces*, vol. 48, no. 5, Sept. 2018, pp. 449–66. *Scopus*, doi:10.1287/inte.2018.0957.
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Vu, Mai-Anh T., et al. “A Shared Vision for Machine Learning in Neuroscience..” *J Neurosci*, vol. 38, no. 7, Feb. 2018, pp. 1601–07. *Pubmed*, doi:10.1523/JNEUROSCI.0508-17.2018.
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Angelino, E., et al. “Learning certifiably optimal rule lists for categorical data.” *Journal of Machine Learning Research*, vol. 18, Jan. 2018, pp. 1–78.

Struck, Aaron F., et al. “Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients..” *Jama Neurology*, vol. 74, no. 12, Dec. 2017, pp. 1419–24. *Epmc*, doi:10.1001/jamaneurol.2017.2459.
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Wang, T., et al. “A Bayesian framework for learning rule sets for interpretable classification.” *Journal of Machine Learning Research*, vol. 18, Aug. 2017, pp. 1–37.

Zeng, J., et al. “Interpretable classification models for recidivism prediction.” *Journal of the Royal Statistical Society. Series A: Statistics in Society*, vol. 180, no. 3, June 2017, pp. 689–722. *Scopus*, doi:10.1111/rssa.12227.
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Letham, Benjamin, et al. “Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]..” *Chaos (Woodbury, N.Y.)*, vol. 27, no. 6, June 2017. *Epmc*, doi:10.1063/1.4986799.
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Bei, Y., et al. “New techniques for preserving global structure and denoising with low information loss in single-image super-resolution.” *Ieee Computer Society Conference on Computer Vision and Pattern Recognition Workshops*, vol. 2018-June, 2018, pp. 987–94. *Scopus*, doi:10.1109/CVPRW.2018.00132.
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Li, O., et al. “Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions.” *32nd Aaai Conference on Artificial Intelligence, Aaai 2018*, 2018, pp. 3530–37.

Ustun, B., and C. Rudin. “Optimized risk scores.” *Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining*, vol. Part F129685, 2017, pp. 1125–34. *Scopus*, doi:10.1145/3097983.3098161.
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Angelino, E., et al. “Learning certifiably optimal rule lists.” *Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining*, vol. Part F129685, 2017, pp. 35–44. *Scopus*, doi:10.1145/3097983.3098047.
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Wang, T., et al. “Bayesian rule sets for interpretable classification.” *Proceedings Ieee International Conference on Data Mining, Icdm*, 2017, pp. 1269–74. *Scopus*, doi:10.1109/ICDM.2016.130.
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Yang, H., et al. “Scalable Bayesian rule lists.” *34th International Conference on Machine Learning, Icml 2017*, vol. 8, 2017, pp. 5971–80.

Letham, B., et al. “Bayesian inference of arrival rate and substitution behavior from sales transaction data with stockouts.” *Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining*, vol. 13-17-August-2016, 2016, pp. 1695–704. *Scopus*, doi:10.1145/2939672.2939810.
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Wang, F., and C. Rudin. “Falling rule lists.” *Journal of Machine Learning Research*, vol. 38, 2015, pp. 1013–22.

Rudin, C. “Turning prediction tools into decision tools.” *Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*, vol. 9355, 2015.

Huggins, J. H., and C. Rudin. “A statistical learning theory framework for supervised pattern discovery.” *Siam International Conference on Data Mining 2014, Sdm 2014*, vol. 1, 2014, pp. 506–14. *Scopus*, doi:10.1137/1.9781611973440.58.
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Demonstrating the real-world impact of interdisciplinary research, a team of two machine learning experts and two neurologists used interpretable models to predict seizures in ICU patients. Duke University's... read more »