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Cynthia D. Rudin

Gilbert, Louis, and Edward Lehrman Distinguished Professor
Computer Science
LSRC D207, Durham, NC 27708

Overview


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.

Current Appointments & Affiliations


Gilbert, Louis, and Edward Lehrman Distinguished Professor · 2024 - Present Computer Science, Trinity College of Arts & Sciences
Professor of Computer Science · 2019 - Present Computer Science, Trinity College of Arts & Sciences
Professor of Electrical and Computer Engineering · 2019 - Present Electrical and Computer Engineering, Pratt School of Engineering
Professor of Statistical Science · 2019 - Present Statistical Science, Trinity College of Arts & Sciences
Professor of Mathematics · 2019 - Present Mathematics, Trinity College of Arts & Sciences
Professor of Biostatistics and Bioinformatics · 2021 - Present Biostatistics & Bioinformatics, Basic Science Departments

In the News


Published March 27, 2025
Six From Duke Named Fellows of the American Association for the Advancement of Science
Published January 14, 2025
Dorothy, We Don’t Use a Floppy Disk Anymore
Published September 27, 2024
Duke 100 Trailblazer: Cynthia Rudin

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Recent Publications


Graph-based design of irregular metamaterials

Journal Article International Journal of Mechanical Sciences · June 1, 2025 In the field of metamaterial research, irregular structures offer a novel and less conventional approach compared to traditional periodic designs. Designing irregular metamaterials is challenging when it comes to ensuring interconnectivity, which is essent ... Full text Cite

A multiscale design method using interpretable machine learning for phononic materials with closely interacting scales

Journal Article Computer Methods in Applied Mechanics and Engineering · May 15, 2025 Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials have sparked promise tunability of elastodynamic waves and vibrations over m ... Full text Cite

Dimension Reduction with Locally Adjusted Graphs

Conference Proceedings of the AAAI Conference on Artificial Intelligence · April 11, 2025 Dimension reduction (DR) algorithms have proven to be extremely useful for gaining insight into large-scale high-dimensional datasets, particularly finding clusters in transcriptomic data. The initial phase of these DR methods often involves converting the ... Full text Cite
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Education, Training & Certifications


Princeton University · 2004 Ph.D.

External Links


Rudin Lab Website