High Dimensional Probabilistic Models Professors in Global

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Debdeep Pati
University of Wisconsin–Madison United States of America
high dimensional network analysis
high-dimensional applications
hierarchical modeling
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Sergei Dudarev
University of Oxford United Kingdom
high-performance computing simulations
mathematical models
stochastic models
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Gerard Gorman
Imperial College London United Kingdom
high-performance computing
multi-scale modeling
multi-physics modeling
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Christoph Reisinger
University of Oxford United Kingdom
non-linear high-dimensional pdes
mathematical computational modeling
deep neural networks
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Jack Lee
King's College London United Kingdom
high-resolution imaging
multiscale modeling
computational modeling
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James Nichols
Australian National University Australia
high dimensional statistics
high dimensional approximation theory
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Yanrong Yang
Australian National University Australia
high dimensional statistics
large dimensional random matrix theory
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Nick Whiteley
University of Bristol United Kingdom
high-dimensional dynamic stochastic systems
probabilistic modelling
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Tony Cai
University of Pennsylvania United States of America
high-dimensional statistics
large-scale inference
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Souvik Dhara
Georgia Institute of Technology United States of America
high-dimensional probability statistics
large-scale networks
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Kun Liang
University of Waterloo Canada
high-dimensional statistics
large-scale inference
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Sen Na
University of California, Berkeley United States of America
high-dimensional estimation and inference
probabilistic graphical models
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Nilabja Guha
University of Manchester United Kingdom
high-dimensional problems
probabilistic graphical models
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Yudong Chen
University of Warwick United Kingdom
high-dimensional statistics
large language models
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Maria Kateri
RWTH Aachen University Germany
high-dimensional statistics
stochastic modelling
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Roger Ghanem
University of Southern California United States of America
probabilistic modeling
probabilistic multi-scale modeling
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Scott Schmidler
Duke University Singapore
high-dimensional sampling algorithms
probabilistic machine learning
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Kaixin Liu
London School of Economics and Political Science United Kingdom
high-dimensional time series
high-frequency time series
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Aswin Sankaranarayanan
Carnegie Mellon University United States of America
high-dimensional signals
low-dimensional models
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Ryan Tibshirani
University of California, Berkeley United States of America
high-dimensional statistics
probabilistic forecasting
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Yiping Guo
McMaster University Canada
probabilistic models
stochastic models
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