Cynthia D. Rudin
Professor of Computer Science
Cynthia Rudin is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics & bioinformatics at Duke University, and directs the Interpretable Machine Learning Lab. 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 the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI). This award, similar only to world-renowned recognitions, such as the Nobel Prize and the Turing Award, carries a monetary reward at the million-dollar level. She is also 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 a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics.
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, AAAI, and ACM SIGKDD. 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 has given keynote/invited talks at several conferences including KDD (twice), AISTATS, CODE, Machine Learning in Healthcare (MLHC), Fairness, Accountability and Transparency in Machine Learning (FAT-ML), ECML-PKDD, and the Nobel Conference. Her work has been featured in news outlets including the NY Times, Washington Post, Wall Street Journal, the Boston Globe, Businessweek, and NPR.
Appointments and Affiliations
- Professor of Computer Science
- Professor of Electrical and Computer Engineering
- Professor of Biostatistics and Bioinformatics
- Professor of Statistical Science
- Office Location: LSRC D342, Durham, NC 27708
- Office Phone: (919) 660-6555
- Ph.D. Princeton University, 2004
Machine learning, interpretability and transparency of predictive models, causal inference, energy, criminal justice, healthcare
- 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: Theory and Algorithms for Machine Learning
- COMPSCI 891: Special Readings in Computer Science
- 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: Theory and Algorithms for Machine Learning
- STA 993: Independent Study
In the News
- Cynthia Rudin Wins Guggenheim Award (Apr 13, 2022 | Pratt School of Engineering)
- The First AI Breast Cancer Sleuth That Shows Its Work (Jan 20, 2022 | Pratt School of Engineering)
- The Need for Transparency and Interpretability at the Intersection of AI and Criminal Justice (Nov 22, 2021 | Duke Government Relations)
- Duke Professor Wins $1 Million Artificial Intelligence Prize, A ‘New Nobel’ (Oct 13, 2021 | Pratt School of Engineering)
- Duke Professor Wins $1 Million Artificial Intelligence Prize, A ‘New Nobel’ (Oct 12, 2021)
- 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)
- Chen, C; Lin, K; Rudin, C; Shaposhnik, Y; Wang, S; Wang, T, A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations, Decision Support Systems, vol 152 (2022) [10.1016/j.dss.2021.113647] [abs].
- Rudin, C; Chen, C; Chen, Z; Huang, H; Semenova, L; Zhong, C, Interpretable machine learning: Fundamental principles and 10 grand challenges, Statistics Surveys, vol 16 (2022), pp. 1-85 [10.1214/21-SS133] [abs].
- Wang, C; Han, B; Patel, B; Rudin, C, In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction, Journal of Quantitative Criminology (2022) [10.1007/s10940-022-09545-w] [abs].
- Guo, Z; Ding, C; Hu, X; Rudin, C, A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables., Physiological Measurement, vol 42 no. 12 (2021) [10.1088/1361-6579/ac3b3d] [abs].
- Barnett, AJ; Schwartz, FR; Tao, C; Chen, C; Ren, Y; Lo, JY; Rudin, C, A case-based interpretable deep learning model for classification of mass lesions in digital mammography, Nature Machine Intelligence, vol 3 no. 12 (2021), pp. 1061-1070 [10.1038/s42256-021-00423-x] [abs].