Random Matrix Models Professors in Global

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Ivan Kryven
Utrecht University Netherlands
random matrices
randomized algorithms
random approximation algorithms
random graph representation
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Pascal Vincent
University of Montreal Canada
statistical models
machine learning
pattern recognition
neural networks
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Nikolai Kühne
Aalborg University Denmark
large language models
machine learning
sequence modelling
neural networks
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Robin Evans
University of Oxford United Kingdom
latent variable models
marginal models
multivariate statistical models
bayesian network models
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Michał Dereziński
University of Michigan United States of America
random matrix theory
randomized numerical linear algebra
machine learning
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Reimer Kühn
King's College London United Kingdom
random matrices
risk modeling
neural networks
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Longxiu Huang
Michigan State University United States of America
random matrix analysis
random tensor analysis
machine learning
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Jan Ambjørn
University of Copenhagen Denmark
matrix models
random paths
random surfaces
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Kai Brüssau
University of Hamburg Germany
regression models
large language models
classification models
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Nicolas Loizou
Johns Hopkins University United States of America
randomized algorithms
randomized numerical linear algebra
machine learning
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Alan Yuille
Johns Hopkins University United States of America
mathematical models
computational models
machine learning
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A.c.c. Coolen
Radboud University Netherlands
random networks
machine learning
neural networks
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Chunfang Lin
Queen's University Canada
simulation models
machine learning
statistical modeling
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Hamed Hassani
University of Pennsylvania United States of America
probabilistic models
machine learning
neural networks
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Laurence Frank
Utrecht University Netherlands
randomized response
machine learning
predictive modelling
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David Duvenaud
University of Toronto Canada
deep probabilistic models
machine learning
generative models
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Sergio Chayet
University of Chicago United States of America
stochastic models
queueing models
game-theoretic models
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Bryon Aragam
University of Chicago Booth School of Business United States of America
probabilistic modeling
deep generative models
latent variable models
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Beidi Chen
Carnegie Mellon University United States of America
large language models
machine learning
machine learning systems
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Jiawei Zhang
University of Wisconsin–Madison United States of America
large language models
machine learning
generative models
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Ziyang Lyu
University of New South Wales Australia
mixed models
machine learning
pattern recognition
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