Cynthia D. Rudin

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.

Education & Training
  • Ph.D., Princeton University 2004

Vu, M-AT, Adalı, T, Ba, D, Buzsáki, G, Carlson, D, Heller, K, Liston, C, Rudin, C, Sohal, VS, Widge, AS, Mayberg, HS, Sapiro, G, and Dzirasa, K. "A Shared Vision for Machine Learning in Neuroscience." The Journal of Neuroscience : the Official Journal of the Society for Neuroscience 38.7 (February 2018): 1601-1607. Full Text

Struck, AF, Ustun, B, Ruiz, AR, Lee, JW, LaRoche, SM, Hirsch, LJ, Gilmore, EJ, Vlachy, J, Haider, HA, Rudin, C, and Westover, MB. "Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients." Jama Neurology 74.12 (December 2017): 1419-1424. Full Text

Wang, T, Rudin, C, Doshi-Velez, F, Liu, Y, Klampfl, E, and MacNeille, P. "A Bayesian framework for learning rule sets for interpretable classification." Journal of Machine Learning Research 18 (August 1, 2017): 1-37.

Letham, B, Letham, PA, Rudin, C, and Browne, EP. "Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]." Chaos (Woodbury, N.Y.) 27.6 (June 2017): 069901-. Full Text

Zeng, J, Ustun, B, and Rudin, C. "Interpretable classification models for recidivism prediction." Journal of the Royal Statistical Society: Series a (Statistics in Society) 180.3 (June 2017): 689-722. Full Text

Ustun, B, Adler, LA, Rudin, C, Faraone, SV, Spencer, TJ, Berglund, P, Gruber, MJ, and Kessler, RC. "The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5." Jama Psychiatry 74.5 (May 2017): 520-526. Full Text

Moghaddass, R, Rudin, C, and Madigan, D. "The factorized self-controlled case series method: An approach for estimating the effects of many drugs on many outcomes." Journal of Machine Learning Research 17 (June 1, 2016).

Letham, B, Letham, PA, Rudin, C, and Browne, EP. "Prediction uncertainty and optimal experimental design for learning dynamical systems." Chaos (Woodbury, N.Y.) 26.6 (June 2016): 063110-null. Full Text

Souillard-Mandar, W, Davis, R, Rudin, C, Au, R, Libon, DJ, Swenson, R, Price, CC, Lamar, M, and Penney, DL. "Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test." Machine Learning 102.3 (March 2016): 393-441. Full Text

Ustun, B, and Rudin, C. "Supersparse linear integer models for optimized medical scoring systems." Machine Learning 102.3 (March 2016): 349-391. Full Text

Pages

Ustun, B, and Rudin, C. "Optimized risk scores." August 13, 2017. Full Text

Angelino, E, Larus-Stone, N, Alabi, D, Seltzer, M, and Rudin, C. "Learning certifiably optimal rule lists." August 13, 2017. Full Text

Wang, T, Rudin, C, Velez-Doshi, F, Liu, Y, Klampfl, E, and Macneille, P. "Bayesian rule sets for interpretable classification." January 31, 2017. Full Text

Yang, H, Rudin, C, and Seltzer, M. "Scalable Bayesian rule lists." January 1, 2017.

Wang, F, and Rudin, C. "Falling rule lists." January 1, 2015.

Tulabandhula, T, and Rudin, C. "On combining machine learning with decision making." October 2014. Full Text

Pages