Theoretical Foundations Of Machine Learning Professors in Global

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Nancy Reid
University of Toronto Canada
statistics policy foundations
theoretical statistics
statistical theory
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Amanda Barnard
Australian National University Australia
applied machine learning
theoretical development
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Di Niu
University of Alberta Canada
applied machine learning
statistical learning
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Xi Chen
New York University United States of America
applied machine learning
statistical learning
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Di Niu
University of Alberta Canada
applied machine learning
statistical learning
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Paul Newton
University of Southern California United States of America
mathematical modeling
machine learning in healthcare
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Mark De Rooij
Leiden University Netherlands
machine learning
data theory
statistical learning
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Norman Zhou
University of Waterloo Canada
mathematical modeling
computational modelling
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Haiyan Liu
Michigan State University United States of America
mathematical modeling
statistical theory
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Rutger Hermsen
Utrecht University Netherlands
mathematical modeling
computational models
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Mert Pilanci
Stanford University United States of America
large scale machine learning
information theory
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Andrey Feuerverger
University of Toronto Canada
machine learning
theoretical statistics
statistical theory
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Pieter Spaas
University of Copenhagen Denmark
mathematical modeling
statistical learning
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Emma Frejinger
Université de Montréal Canada
mathematical modeling
statistical learning
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Dana Yang
Cornell Bowers University United States of America
mathematical modeling
statistical learning
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Miccal Matthews
University of Western Australia Australia
mathematical modeling
theoretical mechanics
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Garvesh Raskutti
University of Wisconsin–Madison United States of America
theoretical statistics
information theory
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Abhirup Banerjee
University of Oxford United Kingdom
geometric machine learning
computational modelling
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Wenlong Mou
University of Toronto Canada
theoretical statistics
statistical learning
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Charbel Farhat
Stanford University United States of America
probabilistic machine learning
mathematical models
computational algorithms
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Alan Yuille
Johns Hopkins University United States of America
machine learning
mathematical models
computational models
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