Cynthia D. Rudin

Cynthia D. Rudin

Professor of Computer Science

External address: 
LSRC D342, Durham, NC 27708
Phone: 
(919) 660-6555

Overview

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.

Education & Training

  • Ph.D., Princeton University 2004

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

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. Full Text

Ustun, B., and C. Rudin. “Learning optimized risk scores.” Journal of Machine Learning Research, vol. 20, June 2019.

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

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

Dieng, Awa, et al. “Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference..” Corr, vol. abs/1806.06802, 2018.

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. Full Text

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.

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. Full Text

Lakkaraju, H., and C. Rudin. “Learning cost-effective and interpretable treatment regimes.” Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Aistats 2017, 2017.

Tulabandhula, T., and C. Rudin. “Robust optimization using machine learning for uncertainty sets.” International Symposium on Artificial Intelligence and Mathematics, Isaim 2014, 2014.

Huggins, J., and C. Rudin. “Toward a theory of pattern discovery.” International Symposium on Artificial Intelligence and Mathematics, Isaim 2014, 2014.

Tulabandhula, T., and C. Rudin. “Generalization bounds for learning with linear and quadratic side knowledge.” International Symposium on Artificial Intelligence and Mathematics, Isaim 2014, 2014.

Tulabandhula, T., and C. Rudin. “Robust optimization using machine learning for uncertainty sets.” International Symposium on Artificial Intelligence and Mathematics, Isaim 2014, 2014.

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