Earl D. McLean, Jr. Professor
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
- Earl D. McLean, Jr. Professor
- Professor of Computer Science
- Professor of Electrical and Computer Engineering
- Professor of Biostatistics and Bioinformatics
- Professor of Statistical Science
Contact Information
- Office Location: LSRC D207, Durham, NC 27708
- Office Phone: +1 919 660 6555
- Websites:
Education
- Ph.D. Princeton University, 2004
Courses Taught
- STA 693: Research Independent Study
- STA 671D: Theory and Algorithms for Machine Learning
- STA 493: Research Independent Study
- STA 393: Research Independent Study
- ME 555: Advanced Topics in Mechanical Engineering
- MATH 494: Research Independent Study
- MATH 491: Independent Study
- ISS 796T: Bass Connections Information, Society & Culture Research Team
- ISS 396T: Bass Connections Information, Society & Culture Research Team
- ECE 687D: Theory and Algorithms for Machine Learning
- ECE 392: Projects in Electrical and Computer Engineering
- COMPSCI 671D: Theory and Algorithms for Machine Learning
- COMPSCI 474: Data Science Competition
- COMPSCI 394: Research Independent Study
- COMPSCI 393: Research Independent Study
- COMPSCI 391: Independent Study
In the News
- Regulating Face Recognition Technology (Mar 26, 2024 | Trinity College of Arts …
- Duke Awards 32 New Distinguished Professorships for 2024 (Mar 19, 2024 | Duke T…
- Duke Faculty Join Federal Roundtable Discussion on AI (Mar 1, 2024 | Pratt Scho…
- Engineering Faculty Help Students Adapt to AI in the Classroom (Oct 20, 2023 | …
- Is the Artificial Intelligence Boom a 'Runaway Train' ? (Feb 24, 2023 | Duke To…
- 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 Sch…
- The Need for Transparency and Interpretability at the Intersection of AI and Cr…
- Duke Professor Wins $1 Million Artificial Intelligence Prize, A ‘New Nobel’ (Oc…
- Duke Professor Wins $1 Million Artificial Intelligence Prize, A ‘New Nobel’ (Oc…
- 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…
- To Save Lives During Seizures, Grab a Scorecard, Machine Learning Style (Dec 10…
- This A.I. Birdwatcher Lets You ‘See’ Through the Eyes of a Machine (Oct 31, 201…
- Stop Gambling with Black Box and Explainable Models on High-Stakes Decisions (M…
- 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 14, 2018)
- Opening the Lid on Criminal Sentencing Software (Jul 19, 2017)
- Data in, Decisions Out: Pratt's Cynthia Rudin Designs Algorithms to Turn Raw In…
- Cynthia Rudin: Training Computers to Find Patterns That Humans Miss (Oct 2, 201…
Representative Publications
- Falcinelli, Shane D., Alicia D. Cooper-Volkheimer, Lesia Semenova, Ethan Wu, Alexander Richardson, Manickam Ashokkumar, David M. Margolis, et al. “Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People With HIV on Suppressive Antiretroviral Therapy.” J Infect Dis 228, no. 11 (November 28, 2023): 1600–1609. https://doi.org/10.1093/infdis/jiad364.
- Garrett, Brandon L., and Cynthia Rudin. “Interpretable algorithmic forensics.” Proceedings of the National Academy of Sciences of the United States of America 120, no. 41 (October 2023): e2301842120. https://doi.org/10.1073/pnas.2301842120.
- Hahn, S., R. Zhu, S. Mak, C. Rudin, and Y. Jiang. “An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation.” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 4089–99, 2023. https://doi.org/10.1145/3580305.3599772.
- Peloquin, J., A. Kirillova, C. Rudin, L. C. Brinson, and K. Gall. “Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning.” Materials and Design 232 (August 1, 2023). https://doi.org/10.1016/j.matdes.2023.112126.
- McDonald, Samantha M., Emily K. Augustine, Quinn Lanners, Cynthia Rudin, L. Catherine Brinson, and Matthew L. Becker. “Applied machine learning as a driver for polymeric biomaterials design.” Nature Communications 14, no. 1 (August 2023): 4838. https://doi.org/10.1038/s41467-023-40459-8.