# Cynthia D. Rudin

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

**External address:**LSRC D342, Durham, NC 27708

**Phone:**(919) 660-6555

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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 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 is a Thomas Langford Lecturer at Duke University during the 2019-2020 academic year.

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

Awan, M. Usaid, et al. “Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference.” *Corr*, vol. abs/2003.00964, 2020.

Fisher, A., et al. “All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously.” *Journal of Machine Learning Research*, vol. 20, Dec. 2019.

Wang, Fulton, et al. “Modeling recovery curves with application to prostatectomy.” *Biostatistics (Oxford, England)*, vol. 20, no. 4, Oct. 2019, pp. 549–64. *Epmc*, doi:10.1093/biostatistics/kxy002.
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Ustun, B., and C. Rudin. “Learning optimized risk scores.” *Journal of Machine Learning Research*, vol. 20, June 2019.

Rudin, Cynthia, and Yaron Shaposhnik. *Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation*. May 2019.

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

Dieng, Awa, et al. “Interpretable Almost-Exact Matching for Causal Inference.” *Proceedings of Machine Learning Research*, vol. 89, Apr. 2019, pp. 2445–53.

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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Usaid Awan, M., et al. “Interpretable almost-matching-exactly with instrumental variables.” *35th Conference on Uncertainty in Artificial Intelligence, Uai 2019*, Jan. 2019.

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, Cynthia. “Do Simpler Models Exist and How Can We Find Them?” *Proceedings of the 25th Acm Sigkdd International Conference on Knowledge Discovery & Data Mining*, ACM, 2019. *Crossref*, doi:10.1145/3292500.3330823.
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Tracà, S., et al. “Reducing exploration of dying arms in mortal bandits.” *35th Conference on Uncertainty in Artificial Intelligence, Uai 2019*, 2019.

Usaid Awan, M., et al. “Interpretable almost-matching-exactly with instrumental variables.” *35th Conference on Uncertainty in Artificial Intelligence, Uai 2019*, 2019.

Tracà, S., et al. “Reducing exploration of dying arms in mortal bandits.” *35th Conference on Uncertainty in Artificial Intelligence, Uai 2019*, 2019.

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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Timofte, Radu, et al. “NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results.” *2018 Ieee/Cvf Conference on Computer Vision and Pattern Recognition Workshops (Cvprw)*, IEEE, 2018. *Crossref*, doi:10.1109/cvprw.2018.00130.
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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.

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.

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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Congratulations to Duke Math faculty member Cynthia Rudin on being awarded a 2020-2021 Energy Research Seed Fund grant. This year the Duke University Energy Initiative program awarded six grants to projects involving 13 faculty from five Duke... read more »

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 »