Cynthia Rudin

Cynthia Diane Rudin (born 1976)[1] is an American computer scientist and statistician specializing in machine learning and known for her work in interpretable machine learning. She is the director of the Prediction Analysis Lab at Duke University, where she is a professor of computer science, electrical and computer engineering, and statistical science.[2]

Education and career

Rudin graduated summa cum laude from the University at Buffalo with a double major in mathematical physics and music theory in 1999.[2] She completed her Ph.D. in applied and computational mathematics at Princeton University in 2004. Her dissertation – entitled Boosting, Margins, and Dynamics – was supervised by Ingrid Daubechies and Robert Schapire.[2][3]

Following positions as a postdoctoral researcher at New York University and a research scientist at Columbia University, she took a faculty position at the MIT Sloan School of Management in 2009, and then moved to Duke University in 2016.[2]

She has served as chair of the Data Mining Section of INFORMS and of the Statistical Learning and Data Science Section of the American Statistical Association.[4]

Recognition

In 2019, Rudin was elected as a Fellow of the American Statistical Association,[2] and of the Institute of Mathematical Statistics "for her contributions to interpretable machine learning algorithms, prediction in large scale medical databases, and theoretical properties of ranking algorithms".[5]

References
  1. Birth year from Library of Congress catalog entry, retrieved 2019-08-22
  2. Curriculum vitae (PDF), retrieved 2019-08-22
  3. Cynthia Rudin at the Mathematics Genealogy Project
  4. "Cynthia Rudin", A Statistician's Life, Celebrating Women in Statistics, AmStat News, March 1, 2019
  5. 2019 IMS Fellows Announced, Institute of Mathematical Statistics, May 14, 2019, retrieved 2019-08-22
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