Cynthia D. Rudin

Professor of Computer Science

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.

Appointments and Affiliations

• Professor of Computer Science
• Professor of Electrical and Computer Engineering
• Professor of Biostatistics and Bioinformatics
• Professor of Statistical Science

Contact Information

• Office Location: LSRC D342, Durham, NC 27708
• Office Phone: (919) 660-6555
• Websites:
  • Rudin Lab Website

Education

• Ph.D. Princeton University, 2004

Courses Taught

• COMPSCI 290: Topics in Computer Science
• COMPSCI 391: Independent Study
• COMPSCI 393: Research Independent Study
• COMPSCI 394: Research Independent Study
• COMPSCI 474: Data Science Competition
• COMPSCI 671D: Machine Learning - Introductory PhD Level
• COMPSCI 891: Special Readings in Computer Science
• ECE 590D: Advanced Topics in Electrical and Computer Engineering
• ECE 687D: Theory and Algorithms for Machine Learning
• ECE 899: Special Readings in Electrical Engineering
• ME 555: Advanced Topics in Mechanical Engineering
• STA 493: Research Independent Study
• STA 671D: Machine Learning - Introductory PhD Level
• STA 993: Independent Study

In the News

• Algorithms That Show Their Work (Aug 30, 2021 | Duke Science & Technology)
• Accurate Neural Network Computer Vision Without The ‘Black Box’ (Dec 15, 2020)
• Artificial Intelligence Makes Blurry Faces Look More Than 60 Times Sharper (Jun 11, 2020)
• To Save Lives During Seizures, Grab a Scorecard, Machine Learning Style (Dec 10, 2019 | Pratt School of Engineering)
• This A.I. Birdwatcher Lets You ‘See’ Through the Eyes of a Machine (Oct 31, 2019)
• Stop Gambling with Black Box and Explainable Models on High-Stakes Decisions (May 21, 2019 | Pratt School of Engineering)
• These Works of Art Were Created by Artificial Intelligence (Mar 18, 2019)
• Duke Team Attempts a Real-Life Version of CSI 'Zoom and Enhance' (Dec 5, 2018)
• Bard or Bot? (Nov 15, 2018)
• Opening the Lid on Criminal Sentencing Software (Jul 19, 2017)
• Data in, Decisions Out: Pratt's Cynthia Rudin Designs Algorithms to Turn Raw Information Into Informed Choices (Mar 15, 2017 | Pratt School of Engineering)
• Cynthia Rudin: Training Computers to Find Patterns That Humans Miss (Oct 2, 2016)

Representative Publications

• Afnan, MAM; Rudin, C; Conitzer, V; Savulescu, J; Mishra, A; Liu, Y; Afnan, M, Ethical Implementation of Artificial Intelligence to Select Embryos in in Vitro Fertilization, Aies 2021 Proceedings of the 2021 Aaai/Acm Conference on Ai, Ethics, and Society (2021), pp. 316-326 [10.1145/3461702.3462589] [abs].
• Wang, T; Morucci, M; Awan, MU; Liu, Y; Roy, S; Rudin, C; Volfovsky, A, FLAME: A fast large-scale almost matching exactly approach to causal inference, Journal of Machine Learning Research, vol 22 (2021)