Cynthia D. Rudin
- Professor of Computer Science
- Associate Professor of Electrical and Computer Engineering (Joint)
- 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.
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. Full Text
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. Full Text
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. Full Text
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. Full Text
Tracà, S., et al. “Reducing exploration of dying arms in mortal bandits.” 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, 2019.
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 Kdd ’19, ACM Press, 2019. Crossref, doi:10.1145/3292500.3330823. Full Text
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. Full Text
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
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.
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. Full Text
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. Full Text
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. Full Text
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