Matrix Models Professors in Global

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Mikael Hedlind
KTH Royal Institute of Technology Sweden
data models
model driven engineering
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Edwin Kolbe
University of Basel Switzerland
microscopic models
statistical model
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Tri Dao
Princeton University United States of America
sequence models
machine learning
structured matrices
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Luke Miller
Radboud University Netherlands
computational modeling
neural network modeling
machine learning
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Leonidas Doumas
University of Edinburgh United Kingdom
computational modeling
neural networks
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Gerasimos Spanakis
Maastricht University Netherlands
computational modeling
machine learning
neural networks
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Jon Willits
University of Illinois United States of America
computational modeling
neural networks
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Kwabena Boahen
Stanford University United States of America
computational modeling
neural networks
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Pablo Debenedetti
Princeton University United States of America
computational modeling
molecular modeling
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Eric Darve
Stanford University United States of America
transformer models
machine learning
large-language models
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Stanford University United States of America
computational modeling
stochastic modeling
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Camila De Souza
Western University Canada
mixed effect models
hierarchical mixture models
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Georges Monette
York University Canada
statistical models
mixed models
longitudinal models
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Andrew Rechnitzer
University of British Columbia Canada
physical models
chemical models
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James Booth
Cornell University Netherlands
mixed models
machine learning
generalized linear models
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Robin Evans
University of Oxford United Kingdom
marginal models
bayesian network models
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Bryon Aragam
University of Chicago Booth School of Business United States of America
probabilistic modeling
deep generative models
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Christian Frier
Aalborg University Denmark
probabilistic modeling
stochastic modeling
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Anna Lombardi
Linköping University Sweden
modeling biological networks
neural networks
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Mikhail Glazunov
Delft University of Technology Netherlands
probabilistic modeling
generative modeling
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Reimer Kühn
King's College London United Kingdom
random matrices
neural networks
risk modeling
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