High Order Automatic Differentiation Professors in Global

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Hai Shu
New York University United States of America
high-dimensional data analysis
machine learning
deep learning
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Kun Liang
University of Waterloo Canada
high-dimensional statistics
machine learning
large-scale inference
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Jelena Diakonikolas
University of Wisconsin–Madison United States of America
first-order optimization algorithms
machine learning
large-scale optimization
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Moez Ali
Queen's University Canada
automated machine learning
machine learning
automated modeling
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Bartolomeo Stellato
Princeton University United States of America
differentiable optimization
machine learning
large scale optimization
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Kaixin Liu
London School of Economics and Political Science United Kingdom
high-dimensional time series
high-frequency time series
machine learning
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Yee Teh
University of Oxford United Kingdom
large scale machine learning
machine learning
deep learning
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Nicolas Loizou
Johns Hopkins University United States of America
machine learning
large-scale optimization
deep learning
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Chuan He
Linköping University Sweden
machine learning
large-scale optimization
deep learning
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Qifan Song
Purdue University United States of America
machine learning
high dimensional data
deep learning
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Mario Ventresca
Purdue University United States of America
automated design inference
machine learning
discrete optimization
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Jianhua Hu
Columbia University United States of America
machine learning
deep learning
high-dimensional genomics and proteomics
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John Doe
New York University United States of America
machine learning
deep learning
algorithm optimization
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Olga Veksler
University of Waterloo Canada
machine learning
deep learning
discrete optimization
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Sylvain Laizet
Imperial College London United Kingdom
high-order finite-difference methods
machine learning
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Uwe Naumann
RWTH Aachen University Germany
algorithmic differentiation
high-performance computing
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Tim Colonius
California Institute of Technology United States of America
high-order-accurate methods
reduced-order modeling
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James Nichols
Australian National University Australia
high dimensional approximation theory
high dimensional statistics
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Guang Cheng
Purdue University United States of America
high dimensional statistical inferences
machine learning
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Dan Nicolae
University of Chicago United States of America
high-dimensional statistical inference
machine learning
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Steven Wang
York University Canada
high dimensional optimization
machine learning
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