Cynthia D. Rudin | Scholars@Duke

Cynthia D. Rudin
Associate Professor of Computer Science

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.

Current Appointments & Affiliations

Associate Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2016
Associate Professor of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2016
Associate Professor of Mathematics, Mathematics, Trinity College of Arts & Sciences 2016
Associate Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2016

Contact Information

Background
Education, Training, & Certifications
- Ph.D., Princeton University 2004
Duke Appointment History
- Scholar in Residence of Computer Science, Computer Science, Trinity College of Arts & Sciences 2016
Recognition
In the News
- JUL 19, 2017
  
  Opening the Lid on Criminal Sentencing Software
- MAR 15, 2017 Pratt School of Engineering
  
  Data in, Decisions Out: Pratt's Cynthia Rudin Designs Algorithms to Turn Raw Information Into Informed Choices
- OCT 2, 2016
  
  Cynthia Rudin: Training Computers to Find Patterns That Humans Miss
Publications & Artistic Works
Selected Publications
- Books
  - Rudin, C. Turning prediction tools into decision tools. January 1, 2015.
- Conference Papers
  - Angelino, E, Larus-Stone, N, Alabi, D, Seltzer, M, and Rudin, C. "Learning certifiably optimal rule lists." August 13, 2017. Full Text
  - Ustun, B, and Rudin, C. "Optimized risk scores." 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
  - Letham, B, Letham, LM, and Rudin, C. "Bayesian inference of arrival rate and substitution behavior from sales transaction data with stockouts." August 13, 2016. Full Text
  - Rudin, C. "Turning prediction tools into decision tools." January 1, 2015.
  - 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
  - Goh, ST, and Rudin, C. "Box drawings for learning with imbalanced data." January 1, 2014. Full Text
  - Huggins, JH, and Rudin, C. "A statistical learning theory framework for supervised pattern discovery." January 1, 2014. Full Text
  - Kim, B, Rudin, C, and Shah, J. "The Bayesian case model: A generative approach for case-based reasoning and prototype classification." January 1, 2014.
  - Wang, D, Passonneau, RJ, Collins, M, and Rudin, C. "Modeling Weather Impact on a Secondary Electrical Grid." 2014. Full Text
  - Wang, T, Rudin, C, Wagner, D, and Sevieri, R. "Learning to detect patterns of crime." October 31, 2013. Full Text
  - Ertekin, S, Rudin, C, and McCormick, TH. "Predicting power failures with reactive point processes." January 1, 2013.
  - Kim, B, and Rudin, C. "Machine learning for meeting analysis." January 1, 2013.
  - Letham, B, Rudin, C, McCormick, TH, and Madigan, D. "An interpretable stroke prediction model using rules and Bayesian analysis." January 1, 2013.
  - Ustun, B, Tracà, S, and Rudin, C. "Supersparse linear integer models for predictive scoring systems." January 1, 2013.
  - Wang, T, Rudin, C, Wagner, D, and Sevieri, R. "Detecting patterns of crime with Series Finder." January 1, 2013.
  - Bertsimas, D, Chang, A, and Rudin, C. "An integer optimization approach to associative classification." December 1, 2012.
  - Ertekin, S, Hirsh, H, and Rudin, C. "Selective sampling of labelers for approximating the crowd." December 1, 2012.
  - Tulabandhula, T, and Rudin, C. "The influence of operational cost on estimation." December 1, 2012.
  - Tulabandhula, T, Rudin, C, and Jaillet, P. "The machine learning and traveling repairman problem." October 31, 2011. Full Text
  - Wu, L, Kaiser, G, Rudin, C, and Anderson, R. "Data quality assurance and performance measurement of data mining for preventive maintenance of power grid." September 15, 2011. Full Text
  - Wu, L, Teravainen, T, Kaiser, G, Anderson, R, Boulanger, A, and Rudin, C. "Estimation of system reliability using a semiparametric model." August 17, 2011. Full Text
  - Mukherjee, I, Rudin, C, and Schapire, RE. "The rate of convergence of AdaBoost." January 1, 2011.
  - Rudin, C, Letham, B, Salleb-Aouissi, A, Kogan, E, and Madigan, D. "Sequential event prediction with association rules." January 1, 2011.
  - Pelossof, R, Jones, M, Vovsha, I, and Rudin, C. "Online coordinate boosting." December 1, 2009. Full Text
  - Radeva, A, Rudin, C, Passonneau, R, and Isaac, D. "Report cards for manholes: Eliciting expert feedback for a learning task." December 1, 2009. Full Text
  - Passonneau, RJ, Rudin, C, Radeva, A, and Liu, ZA. "Reducing noise in labels and features for a real world dataset: Application of NLP corpus annotation methods." July 21, 2009. Full Text
  - Roth, R, Rambow, O, Habash, N, Diab, M, and Rudin, C. "Arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking." December 1, 2008.
  - Rudin, C. "Ranking with a P-norm push." January 1, 2006.
  - Rudin, C, Cortes, C, Mohri, M, and Schapire, RE. "Margin-based ranking meets boosting in the middle." December 1, 2005.
  - Rudin, C, Daubechies, I, and Schapire, RE. "On the Dynamics of Boosting." MIT Press, 2003.
- Journal Articles
  - 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, no. 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, no. 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, no. 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, no. 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, no. 5 (May 2017): 520-526. Full Text
  - Letham, B, Letham, PA, Rudin, C, and Browne, EP. "Prediction uncertainty and optimal experimental design for learning dynamical systems." Chaos (Woodbury, N.Y.) 26, no. 6 (June 2016): 063110-. 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).
  - 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, no. 3 (March 2016): 393-441. Full Text
  - Ustun, B, and Rudin, C. "Supersparse linear integer models for optimized medical scoring systems." Machine Learning 102, no. 3 (March 2016): 349-391. Full Text
  - Ustun, B, Westover, MB, Rudin, C, and Bianchi, MT. "Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms." Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 12, no. 2 (February 2016): 161-168. Full Text
  - Browne, EP, Letham, B, and Rudin, C. "A Computational Model of Inhibition of HIV-1 by Interferon-Alpha." PloS one 11, no. 3 (January 2016): e0152316-. Full Text
  - Ertekin, Ş, and Rudin, C. "A Bayesian Approach to Learning Scoring Systems." Big Data 3, no. 4 (December 2015): 267-276. Full Text
  - Moghaddass, R, and Rudin, C. "The latent state hazard model, with application to wind turbine reliability." The Annals of Applied Statistics 9, no. 4 (December 2015): 1823-1863. Full Text
  - Letham, B, Rudin, C, McCormick, TH, and Madigan, D. "Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model." The Annals of Applied Statistics 9, no. 3 (September 2015): 1350-1371. Full Text
  - Tulabandhula, T, and Rudin, C. "Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge." Machine Learning 100, no. 2-3 (September 2015): 183-216. Full Text
  - Ertekin, Ş, Rudin, C, and McCormick, TH. "Reactive point processes: A new approach to predicting power failures in underground electrical systems." The Annals of Applied Statistics 9, no. 1 (March 2015): 122-144. Full Text
  - Wang, T, Rudin, C, Wagner, D, and Sevieri, R. "Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores." Big Data 3, no. 1 (March 2015): 3-21. Full Text
  - Ertekin, Ş, Rudin, C, and Hirsh, H. "Approximating the crowd." Data Mining and Knowledge Discovery 28, no. 5-6 (September 2014): 1189-1221. Full Text
  - Kim, B, and Rudin, C. "Learning about meetings." Data Mining and Knowledge Discovery 28, no. 5-6 (September 2014): 1134-1157. Full Text
  - Rudin, C, Ertekin, Ş, Passonneau, R, Radeva, A, Tomar, A, Xie, B, Lewis, S, Riddle, M, Pangsrivinij, D, and McCormick, T. "Analytics for Power Grid Distribution Reliability in New York City." Interfaces 44, no. 4 (August 2014): 364-383. Full Text
  - Tulabandhula, T, and Rudin, C. "Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing." Big Data 2, no. 2 (June 2014): 97-112. Full Text
  - Rudin, C, and Wagstaff, KL. "Machine learning for science and society." Machine Learning 95, no. 1 (April 2014): 1-9. Full Text
  - Ban, GY, and Rudin, C. "The Big Data Newsvendor: Practical Insights from Machine Learning." (February 6, 2014).
  - Letham, B, Rudin, C, and Heller, KA. "Growing a list." Data Mining and Knowledge Discovery 27, no. 3 (December 1, 2013): 372-395. Full Text
  - Letham, B, Rudin, C, and Heller, KA. "Growing a list." Data Mining and Knowledge Discovery 27, no. 3 (November 2013): 372-395. Full Text
  - Letham, B, Rudin, C, and Madigan, D. "Sequential event prediction." Machine Learning 93, no. 2-3 (November 2013): 357-380. Full Text
  - Rudin, C, Letham, B, and Madigan, D. "Learning theory analysis for association rules and sequential event prediction." Journal of Machine Learning Research 14 (November 1, 2013): 3441-3492.
  - Mukherjee, I, Rudin, C, and Schapire, RE. "The rate of convergence of AdaBoost." Journal of Machine Learning Research 14 (August 1, 2013): 2315-2347.
  - Tulabandhula, T, and Rudin, C. "Machine learning with operational costs." Journal of Machine Learning Research 14 (June 1, 2013): 1989-2028.
  - Chang, A, Rudin, C, Cavaretta, M, Thomas, R, and Chou, G. "How to reverse-engineer quality rankings." Machine Learning 88, no. 3 (September 2012): 369-398. Full Text
  - McCormick, TH, Rudin, C, and Madigan, D. "Bayesian hierarchical rule modeling for predicting medical conditions." The Annals of Applied Statistics 6, no. 2 (June 2012): 652-668. Full Text
  - Rudin, C, Waltz, D, Anderson, RN, Boulanger, A, Salleb-Aouissi, A, Chow, M, Dutta, H, Gross, PN, Huang, B, Ierome, S, Isaac, DF, Kressner, A, Passonneau, RJ, Radeva, A, and Wu, L. "Machine Learning for the New York City Power Grid." IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 2 (February 2012): 328-345. Full Text
  - Ertekin, S, and Rudin, C. "On equivalence relationships between classification and ranking algorithms." Journal of Machine Learning Research 12 (October 1, 2011): 2905-2929.
  - Rudin, C, Letham, B, Kogan, E, and Madigan, D. "A Learning Theory Framework for Association Rules and Sequential Events." (June 20, 2011).
  - Rudin, C, Passonneau, RJ, Radeva, A, Ierome, S, and Isaac, DF. "21st-Century Data Miners Meet 19th-Century Electrical Cables." Computer 44, no. 6 (June 2011): 103-105. Full Text
  - McCormick, T, Rudin, C, and Madigan, D. "A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction." (January 6, 2011).
  - Rudin, C, Passonneau, RJ, Radeva, A, Dutta, H, Ierome, S, and Isaac, D. "A process for predicting manhole events in Manhattan." Machine Learning 80, no. 1 (July 2010): 1-31. Full Text
  - Rudin, C, and Schapire, RE. "Margin-based ranking and an equivalence between AdaBoost and RankBoost." Journal of Machine Learning Research 10 (November 30, 2009): 2193-2232.
  - Rudin, C. "The P-norm push: A simple convex ranking algorithm that concentrates at the top of the list." Journal of Machine Learning Research 10 (November 30, 2009): 2233-2271.
  - Rudin, C, Schapire, RE, and Daubechies, I. "Analysis of boosting algorithms using the smooth margin function." Annals of Statistics 35, no. 6 (2007): 2723-2768. Full Text
  - Rudin, C, Daubechies, I, and Schapire, RE. "The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins." Journal of Machine Learning Research 5 (2004): 1557-1595.
  - Rudin, C, Schapire, RE, and Daubechies, I. "Boosting based on a smooth margin." Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) 3120 (2004): 502-517.
Teaching & Mentoring
Recent Courses
- COMPSCI 290: Topics in Computer Science 2018
- COMPSCI 394: Research Independent Study 2018
- COMPSCI 571D: Machine Learning 2018
- ECE 682D: Probabilistic Machine Learning 2018
- STA 561D: Probabilistic Machine Learning 2018
- STA 993: Independent Study 2017
- ECE 899: Special Readings in Electrical Engineering 2016

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Education, Training, & Certifications

Duke Appointment History

In the News

Selected Publications

Books

Conference Papers

Journal Articles

Recent Courses