Fast Deterministic Randomized Algorithms Professors in Global

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Gunnar Martinsson
University of Texas at Austin United States of America
efficient algorithms for large data sets
fast solvers
randomized methods
fast multipole methods
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George Papavassilopoulos
University of Southern California United States of America
dynamic stochastic games
parallel algorithms
genetic algorithms
optimization algorithms
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Nicolas Loizou
Johns Hopkins University United States of America
distributed decentralized algorithms
randomized numerical linear algebra
deep learning
large-scale optimization
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Tom Rainforth
University of Oxford United Kingdom
probabilistic programming
probabilistic data-efficient approaches
deep learning
statistical machine learning
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Ivan Kryven
Utrecht University Netherlands
randomized algorithms
random approximation algorithms
random matrices
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Nicole Wein
University of Michigan United States of America
dynamic algorithms
parameterized algorithms
distributed algorithms
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Chris De Sa
Cornell University Netherlands
stochastic algorithms
high-performance machine learning
deep learning
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Chris De Sa
Cornell University Netherlands
stochastic algorithms
high-performance machine learning
deep learning
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Feras Saad
Carnegie Mellon University United States of America
random sampling algorithms
probabilistic programming
automated probabilistic model discovery
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Aaron Bernstein
New York University Tandon School of Engineering United States of America
dynamic algorithms
approximation algorithms
sublinear algorithms
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Gara Pruesse
Vancouver Island University Canada
discrete algorithms
approximation algorithms
optimization algorithms
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Tuğkan Batu
London School of Economics United Kingdom
randomized computation
algorithms
sublinear algorithms
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Debdeep Pati
University of Wisconsin–Madison United States of America
efficient bayesian computation
fair algorithms
real-time tracking algorithms
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Nima Anari
Stanford University United States of America
sampling algorithms
parallel algorithms
algorithms
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Yee Teh
University of Oxford United Kingdom
large scale machine learning
deep learning
probabilistic learning
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Danupon Na Nongkai
KTH Royal Institute of Technology Sweden
approximation algorithms
quantum algorithms
data stream algorithms
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Irina Rish
University of Montreal Canada
probabilistic inference
optimization algorithms
deep learning
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Adji Dieng
Princeton University United States of America
probabilistic modeling
deep learning
statistical machine learning
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Bryon Aragam
University of Chicago Booth School of Business United States of America
probabilistic modeling
deep generative models
statistical machine learning
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Weijun Xie
Georgia Institute of Technology United States of America
approximation algorithms
distributionally robust optimization
stochastic programming
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Nathaniel Bastian
Pennsylvania State University United States of America
federated machine learning
deep learning
computational stochastic optimization
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