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Cynthia D. Rudin, Associate Professor of Computer Science and Electrical and Computer Engineering and Statistical Science and Mathematics

Cynthia D. Rudin

Cynthia Rudin is an associate professor of computer science, electrical and computer engineering, statistical science and mathematics at Duke University, and directs the Prediction Analysis Lab. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD in applied and computational mathematics from Princeton University. She is the recipient of the 2013 and 2016 INFORMS Innovative Applications in Analytics Awards, an NSF CAREER 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. Work from her lab has won 10 best paper awards in the last 5 years. She is past chair of the INFORMS Data Mining Section, and is currently chair of the Statistical Learning and Data Science section of the American Statistical Association. She also serves on (or has served on) committees for DARPA, the National Institute of Justice, the National Academy of Sciences (for both statistics and criminology/law), and AAAI.

Contact Info:
Office Location:  
Office Phone:  (919) 660-6581
Email Address: send me a message

Teaching (Spring 2018):

  • COMPSCI 290.01, TOPICS IN COMPUTER SCIENCE Synopsis
    Allen 103, WF 03:05 PM-04:20 PM
  • STA 561D.001, PROBABILISTIC MACHINE LEARNING Synopsis
    French Sci 2231, WF 10:05 AM-11:20 AM
    (also cross-listed as COMPSCI 571D.001, ECE 682D.001)
  • STA 561D.01D, PROBABILISTIC MACHINE LEARNING Synopsis
    Physics 235, M 03:05 PM-04:20 PM
    (also cross-listed as COMPSCI 571D.01D, ECE 682D.01D)
  • STA 561D.02D, PROBABILISTIC MACHINE LEARNING Synopsis
    Bio Sci 130, M 10:05 AM-11:20 AM
    (also cross-listed as COMPSCI 571D.02D, ECE 682D.02D)
  • STA 561D.03D, PROBABILISTIC MACHINE LEARNING Synopsis
    Physics 235, M 11:45 AM-01:00 PM
    (also cross-listed as COMPSCI 571D.03D, ECE 682D.03D)
  • STA 561D.04D, PROBABILISTIC MACHINE LEARNING Synopsis
    Physics 259, M 01:25 PM-02:40 PM
    (also cross-listed as COMPSCI 571D.04D, ECE 682D.04D)
  • STA 561D.05D, PROBABILISTIC MACHINE LEARNING Synopsis
    Bio Sci 154, M 11:45 AM-01:00 PM
    (also cross-listed as COMPSCI 571D.05D, ECE 682D.05D)
  • STA 561D.06D, PROBABILISTIC MACHINE LEARNING Synopsis
    Bio Sci 063, M 08:30 AM-09:45 AM
    (also cross-listed as COMPSCI 571D.06D, ECE 682D.06D)
Education:

Ph.D.Princeton University2004
Recent Grant Support

  • QuBBD: Collaborative Research: Matching Methods for causal inference: big data and networks, National Institutes of Health, 1-R01EB025021-01, 2017/09-2020/06.      

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320