118:. She served on the ISAT faculty advisory board for DARPA, was a councilor for AAAI, and a member of the Bureau of Justice Assistance Criminal Justice Technology Forecasting Group (BJA CJTFG). She currently serves on the executive committee member for ACM SIGKDD, and is a member of both the Committee on Applied and Theoretical Statistics (CATS) and the Committee on Law and Justice (CLAJ) of the National Academies of Sciences, Engineering, and Medicine. She is an associate editor for Management Science, the Harvard Data Science Review and the Journal of Quantitative Criminology.
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use
Interpretable Models Instead," Nature Machine Intelligence, 2019, outlines several other reasons. She has led several efforts to encourage work on societal good applications in machine learning, including editing the Special Issue on Machine Learning for Science and Society in the Machine Learning journal , and organizing the American Statistical Association's report "Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society." Her work is an influential source for the
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148:, the 2016 INFORMS Innovative Applications in Analytics Award for work on interpretable machine learning models for assessing cognitive decline, and the 2019 INFORMS Innovative Applications in Analytics Award for work on interpretable machine learning models for seizure prediction in critically ill patients, leading to the 2HELPS2B score used in intensive care units.
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Rudin is well known for her work critiquing black box models in the criminal justice system and for high-stakes decisions, on the grounds that interpretable models can be constructed that are equally accurate. Her work "Stop
Explaining Black Box Machine Learning Models for High Stakes Decisions and
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Rudin's work on scoring systems with former student Berk Ustun was used for developing medical scoring systems for sleep apnea screening and diagnosis, for seizure prediction in ICU patients, for ADHD screening in adults, and for detection of cognitive decline using handwriting analysis (the Clock
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Starting in 2007, Rudin was the lead scientist on a collaborative project between
Columbia University and Con Edison to use machine learning to maintain New York City's secondary electrical distribution network. This project was awarded the 2013 INFORMS Innovative Applications in Analytics Award.
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Along with student Tong Wang and detectives from the
Cambridge Police Department, Cambridge MA, Rudin developed the Series Finder algorithm for crime series detection. Series Finder was built into the Patternizr algorithm used by the NYPD to detect patterns of crime committed by the same
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In her graduate work at
Princeton, Rudin proved convergence properties of boosting algorithms. Her PhD thesis answered a well-studied question of whether AdaBoost maximizes the L1 margin, which is a type of distance between a decision boundary and the data observation closest to it.
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At Duke, Rudin coached two teams of undergraduate students who won the 2018 NTIRE Single Image
Superresolution Competition (Track 1, classic bicubic), and the 2018 PoeTix Literary Turing Competition.
134:"for her contributions to interpretable machine learning algorithms, prediction in large scale medical databases, and theoretical properties of ranking algorithms". She was elected as a Fellow of the
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Rudin was the co-winner of the
Manufacturing and Service Operations Management Best Operations Management paper in Operations Research Award from INFORMS in 2021.
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Discovery with Data: Leveraging
Statistics with Computer Science to Transform Science and Society, A Working Group of the American Statistical Association
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Drawing test). This work earned the 2016 and 2019 INFORMS Innovative
Applications in Analytics Award, and was a finalist for the Wagner Prize.
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Rudin was a winner of the FICO Recognition Award for the
Explainable Machine Learning Challenge in 2018.
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Rudin was a Finalist for 2017 Daniel H. Wagner Prize for Excellence in Operations Research in 2017.
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Rudin was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015.
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A Recommendation Engine to Aid in Identifying Crime Patterns, by Alex Chohlas-Wood and E.S. Levine
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Learning to Detect Patterns of Crime, by Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri
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Rudin has given keynotes talks at KDD (2014 and 2019) , AISTATS , and the Nobel Conference (2021)
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She received the 2013 INFORMS Innovative Applications in Analytics Award for her work on
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American Statistical Association Statistical Learning and Data Science Section Officers
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32:(born 1976) is an American computer scientist and statistician specializing in
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in 1999. She completed her Ph.D. in applied and computational mathematics at
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Fellows of the Association for the Advancement of Artificial Intelligence
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40:. She is the director of the Interpretable Machine Learning Lab at
728:"Meet Cynthia Rudin A Champion of Interpretable Machine Learning"
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for Artificial Intelligence for the Benefit of Humanity from the
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and of the Statistical Learning and Data Science Section of the
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12 of the Most Impressive Professors at MIT, Business Insider
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She received the prestigious Guggenheim Fellowship in 2022.
767:, A Statistician's Life, Celebrating Women in Statistics,
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Association for the Advancement of Artificial Intelligence
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Association for the Advancement of Artificial Intelligence
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Association for the Advancement of Artificial Intelligence
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She has served as chair of the Data Mining Section of
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FICO Announces Winners of the Inaugural XML Challenge
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NASEM Committee on Applied and Theoretical Statistics
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in 2009, and then moved to Duke University in 2016.
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Following positions as a postdoctoral researcher at
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Fellows of the Institute of Mathematical Statistics
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Journal of Quantitative Criminology Editorial Board
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126:In 2019, Rudin was elected as a Fellow of the
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422:"Harvard Data Science Review Editorial Board"
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16:American computer scientist and statistician
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522:Announcing the 2022 Guggenheim Fellows
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80:in 2004. Her dissertation – entitled
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213:Library of Congress catalog entry
82:Boosting, Margins, and Dynamics –
551:INFORMS search for Cynthia Rudin
357:2021 ACM SIGKDD Election Results
128:American Statistical Association
116:American Statistical Association
596:ACM websearch for Cynthia Rudin
293:from the original on 2021-10-17
272:from the original on 2021-10-15
105:MIT Sloan School of Management
38:interpretable machine learning
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744:10.1080/09332480.2020.1754062
314:Mathematics Genealogy Project
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122:Additional awards and honours
900:University at Buffalo alumni
895:American women statisticians
880:American computer scientists
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945:21st-century American women
905:Princeton University alumni
666:Nature Machine Intelligence
426:Harvard Data Science Review
146:electrical grid reliability
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470:2019 IMS Fellows Announced
36:and known for her work in
910:Columbia University staff
688:10.1038/s42256-019-0048-x
940:American women academics
834:publications indexed by
920:Duke University faculty
660:Rudin, Cynthia (2019),
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68:with a double major in
885:American statisticians
215:, retrieved 2019-08-22
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611:AISTATS 2017 Schedule
66:University at Buffalo
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496:Elected AAAI Fellows
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78:Princeton University
70:mathematical physics
56:Education and career
455:List of ASA Fellows
101:Columbia University
97:New York University
30:Cynthia Diane Rudin
84:was supervised by
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86:Ingrid Daubechies
46:Squirrel AI Award
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870:1976 births
769:AmStat News
191:community.
864:Categories
738:(2): 4–6,
679:1811.10154
507:2022-04-10
481:2019-08-22
408:2019-08-22
297:2021-10-18
276:2021-10-18
247:2022-04-10
199:References
138:in 2022.
827:Home page
752:218845746
714:182656421
64:from the
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291:archived
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312:at the
112:INFORMS
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811:(PDF)
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