Reduced Modeling Professors in Global

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Magnus Röding
Chalmers University of Technology Sweden
physical modeling
stochastic modeling
statistical modeling
machine learning
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Samir Maity
Aalborg University Denmark
predictive modeling
imprecise modeling
language modeling
machine learning
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Arson Lab
Cornell University Netherlands
model reduction
numerical modeling
multi-scale modeling
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Fangxin Fang
Imperial College London United Kingdom
model reduction
predictive modeling
machine learning
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Daniel Graham
Imperial College London United Kingdom
mathematical modelling
statistical modelling
economic modelling
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Till Bretschneider
University of Warwick United Kingdom
image-based modeling
generative modeling
machine learning
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Jaime Ide
Yale University United States of America
theoretical modeling
predictive modeling
machine learning
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Luke Miller
Radboud University Netherlands
computational modeling
neural network modeling
machine learning
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Kamal Hossain
University of Melbourne Australia
computational modeling
statistical modeling
machine learning
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Reem Khir
Purdue University United States of America
constrained optimization
system modeling
machine learning
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Joakim Munkhammar
Uppsala University Sweden
mathematical modeling
statistical modeling
machine learning
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Da Zhao
University of Melbourne Australia
statistical modeling
predictive modeling
machine learning
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Jeff Bilmes
University of Washington United States of America
statistical modeling
graphical modeling
machine learning
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Jeff Bilmes
University of Washington United States of America
statistical modeling
graphical modeling
machine learning
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Yalan Song
Pennsylvania State University United States of America
predictive modeling
differentiable modeling
machine learning
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Zia Farooq
Umeå University Sweden
predictive modelling
dynamic modeling
machine learning
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James Kermode
University of Warwick United Kingdom
predictive modelling
materials modelling
machine learning
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Chunfang Lin
Queen's University Canada
statistical modeling
simulation models
machine learning
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Junyao Xie
University of Guelph Canada
predictive modeling
dynamic modeling
machine learning
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Markus Buehler
Massachusetts Institute of Technology United States of America
multiscale modeling
atomistic modeling
machine learning
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Prashant Singh
Uppsala University Sweden
inverse modeling
constrained multi-objective optimization
machine learning
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