High Dimensional Learning Problems Professors in Global

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Qiang Liu
University of Texas at Austin United States of America
deep distributed learning
big data problems
deep reinforcement learning
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Debdeep Pati
University of Wisconsin–Madison United States of America
high dimensional network analysis
high-dimensional applications
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Moses Charikar
Stanford University United States of America
efficient algorithms for computational problems in high-dimensional statistics
efficient algorithmic techniques for processing high-dimensional data sets
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David Thomas
University of Southampton United Kingdom
high-level synthesis
high-performance computing
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Andrew Winters
Linköping University Sweden
high-performance computing
simulation problems
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Thomas Ryberg
Aalborg University Denmark
problem based learning
networked learning
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Antoine Van Den Beemt
Eindhoven University of Technology Belgium
challenge-based learning
networked learning
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Uwe Naumann
RWTH Aachen University Germany
high-performance computing
combinatorial problems
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Christoph Meier
Technical University of Munich Germany
high-performance computing
high-fidelity multi-physics modeling
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Nozomu Yachie
University of British Columbia Canada
high-performance computing
high-resolution imaging
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Jan Vermunt
Eindhoven University of Technology Belgium
challenge-based learning
student learning
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Bernd Dammann
Technical University of Denmark Denmark
high-performance computing
large scale computations
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Eric Darve
Stanford University United States of America
high-performance computing
large-language models
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Rayhaneh Akhavan
University of Michigan United States of America
high fidelity computations
high performance computing
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Shimon Whiteson
University of Oxford United Kingdom
deep reinforcement learning
imitation learning
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Shimon Whiteson
University of Oxford United Kingdom
deep reinforcement learning
imitation learning
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Giacomo Spigler
Tilburg University Netherlands
deep reinforcement learning
imitation learning
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Charles Kemp
University of Melbourne Australia
computational models of learning
relational learning
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Mark Schmidt
University of British Columbia Canada
structured prediction problems
large-scale machine learning
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Jörg Liesen
Technical University of Berlin Germany
eigenvalue problems
large linear systems
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Massimo Fornasier
Technical University of Munich Germany
high-dimensional optimization
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