Machine Learning Approaches Professors in Global

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Jennifer O'neil
University of Ottawa Canada
co-designed approaches
mixed-method participatory approaches
person-centred approaches
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Eric Rosen
University of Michigan United States of America
neural network approaches
learning algorithms
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Ilan Adler
University of California, Berkeley United States of America
geometrical approaches
combinatorial approaches
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Jorge Almeida
University of Texas at Austin United States of America
neurobiological approaches
behavioral approaches
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Chao Tian
Texas A&M University United States of America
computational approach
approximate approaches
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Katerina Stavrianaki
University College London United Kingdom
experimental approaches
geophysical approaches
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Astrid Brousselle
University of Victoria Canada
collaborative approaches
participative approaches
community-based approaches
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Susie Rao
ETH Zurich Switzerland
multimodal machine learning
pattern mining
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May Lim
Singapore Institute of Technology Singapore
learning approaches
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Fei Lab
University of Chicago United States of America
sequencing approaches
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Chenxi Huang
Yale University United States of America
computational approaches
mathematical approaches
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Imre Pázsit
Chalmers University of Technology Sweden
machine learning methods
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Xiao Liu
University of Texas at Austin United States of America
machine learning methods
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Sergey Kravchenko
University of British Columbia Canada
machine learning methods
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Ashwini Ashokkumar
Harvard University United States of America
big data approaches
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Fani Boukouvala
Georgia Institute of Technology United States of America
big data approaches
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Ivana Savic
King's College London United Kingdom
theoretical computational approaches
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Priya Pathak
University of New South Wales Australia
assessment approaches
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Ashish Sharma
University of New South Wales Australia
stochastic approaches
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Pierre-luc Paquet
University of Montreal Canada
multilingual approaches
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Jake Spicer
University of Warwick United Kingdom
sampling approaches
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