Cynthia D. Rudin, Gilbert, Louis, and Edward Lehrman Distinguished 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.

Please note: Cynthia has left the Mathematics department at Duke University; some info here might not be up to date.

Contact Info:

Office Location:	LSRC D207, Durham, NC 27708
Office Phone:	(919) 660-6555
Email Address:

Teaching (Fall 2024):

• COMPSCI 671D.001, THEORY & ALG MACHINE LEARNING Synopsis

  Bio Sci 111, TuTh 10:05 AM-11:20 AM

  (also cross-listed as ECE 687D.001, STA 671D.001)

• COMPSCI 671D.01D, THEORY & ALG MACHINE LEARNING Synopsis

  Physics 130, M 10:05 AM-11:20 AM

  (also cross-listed as ECE 687D.01D, STA 671D.01D)

• COMPSCI 671D.02D, THEORY & ALG MACHINE LEARNING Synopsis

  Physics 130, M 10:05 AM-11:20 AM

  (also cross-listed as ECE 687D.02D, STA 671D.02D)

• COMPSCI 671D.03D, THEORY & ALG MACHINE LEARNING Synopsis

  Physics 130, M 10:05 AM-11:20 AM

  (also cross-listed as ECE 687D.03D, STA 671D.03D)

• COMPSCI 671D.04D, THEORY & ALG MACHINE LEARNING Synopsis

  Physics 128, M 11:45 AM-01:00 PM

  (also cross-listed as ECE 687D.04D, STA 671D.04D)

• COMPSCI 671D.05D, THEORY & ALG MACHINE LEARNING Synopsis

  Physics 128, M 11:45 AM-01:00 PM

  (also cross-listed as ECE 687D.05D, STA 671D.05D)

• COMPSCI 671D.06D, THEORY & ALG MACHINE LEARNING Synopsis

  Physics 128, M 11:45 AM-01:00 PM

  (also cross-listed as ECE 687D.06D, STA 671D.06D)

• COMPSCI 671D.07D, THEORY & ALG MACHINE LEARNING Synopsis

  LSRC A247, M 01:25 PM-02:40 PM

  (also cross-listed as ECE 687D.07D, STA 671D.07D)

• COMPSCI 671D.08D, THEORY & ALG MACHINE LEARNING Synopsis

  LSRC A247, M 01:25 PM-02:40 PM

  (also cross-listed as ECE 687D.08D, STA 671D.08D)

• COMPSCI 671D.09D, THEORY & ALG MACHINE LEARNING Synopsis

  LSRC A247, M 01:25 PM-02:40 PM

  (also cross-listed as ECE 687D.09D, STA 671D.09D)

• COMPSCI 671D.10D, THEORY & ALG MACHINE LEARNING Synopsis

  Bio Sci 155, M 03:05 PM-04:20 PM

  (also cross-listed as ECE 687D.10D, STA 671D.10D)

• COMPSCI 671D.11D, THEORY & ALG MACHINE LEARNING Synopsis

  Bio Sci 155, M 03:05 PM-04:20 PM

  (also cross-listed as ECE 687D.11D, STA 671D.11D)

• COMPSCI 474.01, DATA SCIENCE COMPETITION Synopsis

  LSRC D243, TuTh 11:45 AM-01:00 PM

• COMPSCI 474.02, DATA SCIENCE COMPETITION Synopsis

  LSRC D243, TuTh 07:00 PM-08:15 PM

Education:

Ph.D.

Princeton University

2004

Recent Publications   (More Publications)

1. Zhang, H; Mahabadi, RK; Rudin, C; Guilleminot, J; Brinson, LC, Uncertainty quantification of acoustic metamaterial bandgaps with stochastic material properties and geometric defects, Computers and Structures, vol. 305 (December, 2024) [doi]  [abs]
2. Chen, SF; Guo, Z; Ding, C; Hu, X; Rudin, C, Sparse learned kernels for interpretable and efficient medical time series processing, Nature Machine Intelligence, vol. 6 no. 10 (October, 2024), pp. 1132-1144 [doi]  [abs]
3. Semenova, L; Wang, Y; Falcinelli, S; Archin, N; Cooper-Volkheimer, AD; Margolis, DM; Goonetilleke, N; Murdoch, DM; Rudin, CD; Browne, EP, Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy., Elife, vol. 13 (September, 2024) [doi]  [abs]
4. Hahn, S; Yin, J; Zhu, R; Xu, W; Jiang, Y; Mak, S; Rudin, C, SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (August, 2024), pp. 5050-5060 [doi]  [abs]
5. Ding, C; Guo, Z; Chen, Z; Lee, RJ; Rudin, C; Hu, X, SiamQuality: a ConvNet-based foundation model for photoplethysmography signals., Physiological measurement, vol. 45 no. 8 (August, 2024) [doi]  [abs]