Physics Informed Machine Learning Professors in Global

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Tal Kachman
Radboud University Netherlands
machine learning
financial machine learning
statistical physics
reinforcement learning
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Vinay Gogineni
University of Southern Denmark Denmark
physics-informed neural networks
quantum machine learning
machine learning
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Clemens Gühmann
Technical University of Berlin Germany
physics-based modelling
machine learning
data-based modelling
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Michiel Van De Panne
University of British Columbia Canada
physics-based simulation
machine learning
reinforcement learning
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Moez Ali
Queen's University Canada
automated machine learning
machine learning
automated modeling
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Yisong Yue
California Institute of Technology United States of America
statistical machine learning
interactive machine learning
structured machine learning
machine learning
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Danica Sutherland
University of British Columbia Canada
statistical machine learning
theoretical machine learning
machine learning
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Hina Shaheen
University of Manitoba Canada
statistical machine learning
machine learning
computational modelling
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Navid Azizan
Massachusetts Institute of Technology United States of America
scientific machine learning
machine learning
trustworthy machine learning
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Stephanie Milani
Johns Hopkins University United States of America
interactive machine learning
machine learning
reinforcement learning
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Martin Kröger
ETH Zurich Switzerland
computational physics
statistical physics
theoretical physics
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Matthew Howard
King's College London United Kingdom
machine learning
imitation learning
reinforcement learning
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Giorgia Ramponi
University of Zurich Switzerland
machine learning
imitation learning
reinforcement learning
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Florian Shkurti
University of Toronto Canada
machine learning
imitation learning
reinforcement learning
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Nicholas Rhinehart
University of Toronto Canada
machine learning
imitation learning
reinforcement learning
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Florian Shkurti
University of Toronto Canada
machine learning
imitation learning
reinforcement learning
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Rickard Armiento
Linköping University Sweden
machine learning
physical modeling
theoretical physics
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Chad Williams
University of Victoria Canada
machine learning
computational modelling
reinforcement learning
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David Brückner
University of Basel Switzerland
machine learning
statistical physics
theoretical physics
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Girish Chowdhary
University of Illinois United States of America
machine learning
machine vision
reinforcement learning
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Jeffrey Schenker
Michigan State University United States of America
mathematical physics
statistical physics
theoretical physics
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