Random Matrices Professors in Global

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Ivan Kryven
Utrecht University Netherlands
random matrices
randomized algorithms
random graph representation
random approximation algorithms
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Matthias Winkel
University of Oxford United Kingdom
random hierarchies
random trees
random partitions
tree-valued random processes
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Jan Ambjørn
University of Copenhagen Denmark
random paths
random surfaces
matrix 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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Matthew Jenssen
King's College London United Kingdom
random matrix theory
algorithms
probabilistic
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Guillaume Conchon--kerjan
King's College London United Kingdom
random environment
random trees
random walks
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A.c.c. Coolen
Radboud University Netherlands
random networks
machine learning
neural networks
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Elchanan Mossel
Massachusetts Institute of Technology United States of America
randomized algorithms
markov random fields
algorithms
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Thomas Sauerwald
University of Cambridge United Kingdom
randomized algorithms
random walks
machine learning
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Sergey Ponomarenko
Dalhousie University Canada
random lasers
extreme random events
random walks
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Muller
Western University Canada
random graph theory
algorithms
mathematical models
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Paul Mcnicholas
McMaster University Canada
matrix variate distributions
pattern recognition
machine learning
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Javad Ghaderi
Columbia University United States of America
algorithms
machine learning
stochastic networks
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John Oommen
Carleton University Canada
pattern recognition
neural networks
statistical pattern recognition
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Pascal Vincent
University of Montreal Canada
pattern recognition
machine learning
neural networks
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D.m.j. Tax
Delft University of Technology Netherlands
pattern recognition
machine learning
neural networks
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Anders Karlsson
University of Geneva Switzerland
random walks
machine learning
neural networks
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Yang Zhang
University of Manitoba Canada
matrix tensor theory
machine learning
neural networks
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Ziyang Lyu
University of New South Wales Australia
pattern recognition
machine learning
crossed random effects
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Keith Jenkins
University of Southern California United States of America
pattern recognition
machine learning
neural networks
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Mahdi Khodayar
The University of Tulsa Iran
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machine learning
statistical pattern recognition
probabilistic graphical models
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