Physics Aware Machine Learning Professors in Global

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Nicholas Rhinehart
University of Toronto Canada
machine learning
robot learning
deep learning
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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Satyasaran Changdar
University of Copenhagen Denmark
physics-informed neural networks
machine learning
deep learning
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Clemens Gühmann
Technical University of Berlin Germany
physics-based modelling
machine learning
data-based modelling
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Stacy Tantum
Duke University Singapore
physics-based statistical signal processing
context-aware machine learning
mindset-aware learning
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Stacy Tantum
Duke University Singapore
physics-based statistical signal processing
context-aware machine learning
mindset-aware learning
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Michiel Van De Panne
University of British Columbia Canada
physics-based simulation
machine learning
reinforcement learning
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Nathaniel Bastian
Pennsylvania State University United States of America
federated machine learning
machine learning
deep learning
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Yisong Yue
California Institute of Technology United States of America
machine learning
statistical machine learning
interactive machine learning
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Danica Sutherland
University of British Columbia Canada
machine learning
statistical machine learning
theoretical machine learning
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Samy Bengio
École Polytechnique Fédérale de Lausanne Switzerland
machine learning
statistical machine learning
deep learning
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Girish Chowdhary
University of Illinois United States of America
machine learning
machine vision
reinforcement learning
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Kristen Grauman
University of Texas at Austin United States of America
machine learning
interactive machine learning
deep learning
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Benjamin Joffe
Georgia Institute of Technology United States of America
machine learning
robot learning
deep learning
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Stephanie Milani
Johns Hopkins University United States of America
machine learning
interactive machine learning
reinforcement learning
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Lerrel Pinto
New York University United States of America
machine learning
robot learning
reinforcement learning
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Yangchen Pan
University of Oxford United Kingdom
machine learning
deep learning
reinforcement learning
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Sidney Givigi
Queen's University Canada
machine learning
deep learning
reinforcement learning
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Hongyang Dong
University of Warwick United Kingdom
machine learning
deep learning
reinforcement learning
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Mohsen Amiri
Stockholm University Sweden
machine learning
deep learning
reinforcement learning
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Tal Kachman
Radboud University Netherlands
machine learning
deep learning
reinforcement learning
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