Spaced Learning Professors in Global

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Antoine Van Den Beemt
Eindhoven University of Technology Belgium
networked learning
transdisciplinary learning
technology enhanced learning
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Stig Enemark
Aalborg University Denmark
learning
problem based learning
spatial planning
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Yuhong Guo
Carleton University Canada
self-supervised learning
semi-supervised learning
representation learning
federated learning
reinforcement learning
unsupervised learning
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Yuhong Guo
Carleton University Canada
self-supervised learning
semi-supervised learning
representation learning
federated learning
reinforcement learning
unsupervised learning
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Lone Hersted
Aalborg University Denmark
action learning
learning processes
organizational learning
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Nick Turk-browne
Yale University United States of America
statistical learning
learning
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Anthony Coache
Imperial College London United Kingdom
statistical learning
reinforcement learning
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Alexander Rakhlin
Massachusetts Institute of Technology United States of America
statistical learning
reinforcement learning
online learning
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Susan Murphy
Harvard John Paulson School of Engineering and Applied Sciences United States of America
statistical learning
reinforcement learning
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Thomas Ryberg
Aalborg University Denmark
networked learning
problem based learning
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Valerie Irvine
University of Victoria Canada
networked learning
learning design
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Stefan Kachel
University of Melbourne Australia
statistical learning
categorical learning
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Violeta Iosub
University of Victoria Canada
self-directed learning
collaborative learning
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Longhai Li
University of Saskatchewan Canada
statistical learning
statistical machine learning
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Yalini Easvaralingam
Monash University Australia
statistical learning
unsupervised learning
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Mina Aminghafari
University of Calgary Canada
statistical learning
unsupervised learning
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Joris Bierkens
Delft University of Technology Netherlands
statistical learning
stochastic simulation
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Benedek Kurdi
University of Illinois United States of America
learning
reinforcement learning
causal learning
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Qingyun Wu
Pennsylvania State University United States of America
learning
reinforcement learning
automated machine learning
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Joseph Williams
University of Toronto Canada
learning
reinforcement learning
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Tomas Nordström
Umeå University Sweden
collaborative learning
federated learning
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