Fast Deterministic Randomized Algorithms Professors in Global

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Frank Wood
University of British Columbia Canada
probabilistic programming
deep generative modeling
applied probabilistic machine learning
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Tom Rainforth
University of Oxford United Kingdom
probabilistic programming
probabilistic data-efficient approaches
deep learning
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Xiucai Ding
University of California, Davis United States of America
randomized numerical linear algebra
stochastic optimization
deep learning
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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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Debdeep Pati
University of Wisconsin–Madison United States of America
efficient bayesian computation
fair algorithms
real-time tracking algorithms
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Bahar Taskesen
University of Chicago Booth School of Business United States of America
large-scale stochastic optimization
algorithmic fairness
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Kavita Ramanan
Brown University United States of America
high-dimensional probability
stochastic networks
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Danupon Na Nongkai
KTH Royal Institute of Technology Sweden
approximation algorithms
quantum algorithms
data stream algorithms
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Kemal Büyükkurt
Concordia University Canada
probabilistic choice models
probabilistic decision support systems
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Kai James
Georgia Institute of Technology United States of America
high-fidelity computational models
algorithms
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Pravesh Kothari
Princeton University United States of America
high dimensional probability
random matrices
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Siamak Mehrkanoon
Utrecht University Netherlands
numerical algorithms
deep learning
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P.a.n. Bosman
Delft University of Technology Netherlands
evolutionary algorithms
genetic algorithms
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Boris Rubinsky
University of California, Berkeley United States of America
evolutionary algorithms
genetic algorithms
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Tarek Hegazi
University of Waterloo Canada
evolutionary algorithms
genetic algorithms
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Christian Jacob
University of Calgary Canada
evolutionary algorithms
genetic algorithms
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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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Ryan Tibshirani
University of California, Berkeley United States of America
distribution-free inference
probabilistic forecasting
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Tahereh Koohi
University of New South Wales Australia
evolutionary algorithms
deep learning
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Marcus Michelen
Northwestern University United States of America
random matrices
random polynomials
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Daniel Roy
University of Toronto Canada
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probabilistic programming
deep learning
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