Bayesian Uncertainty Learning Professors in Global

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Yee Teh
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
probabilistic learning
machine learning
deep learning
large scale machine learning
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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian graphical models
bayesian methods
efficient bayesian computation
approximate bayesian methods
machine learning
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Mayank Chadha
University of California, San Diego United States of America
bayesian optimization
bayesian decision theory
machine learning
uncertainty quantification
geometrically exact modeling
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Mehrdad Mahdavi
Pennsylvania State University United States of America
large-scale machine learning
computational statistical machine learning
computational learning theory
deep learning
statistical learning theory
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Yanan Fan
University of New South Wales Australia
bayesian computation
bayesian inference
bayesian nonparametrics
approximate bayesian computation
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Sharmistha Guha
Texas A&M University United States of America
bayesian high dimensional regression
bayesian causal inference
bayesian object-oriented regression
bayesian nonparametric mixture models
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Umberto Picchini
Chalmers University of Technology Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
statistical inference
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Umberto Picchini
University of Gothenburg Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
statistical inference
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Michele Guindani
University of California, Irvine United States of America
bayesian modeling
nonparametric bayesian models
machine learning
statistical decision-making under uncertainty
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Geoff Pleiss
University of British Columbia Canada
bayesian optimization
probabilistic machine learning
machine learning
uncertainty quantification
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Hina Shaheen
University of Manitoba Canada
bayesian inference
machine learning
statistical machine learning
computational modelling
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Peter Mueller
University of Texas at Austin United States of America
bayesian bioinformatics
nonparametric bayesian inference
computational methods in bayesian inference
bayesian adaptive clinical trial design
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Jaehee Kim
Cornell University Netherlands
bayesian inference
machine learning
mathematical models
statistical inference
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Prasanth Nair
University of Toronto Institute for Aerospace Studies Canada
bayesian model calibration
machine learning
uncertainty quantification
computational modeling
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Irina Rish
University of Montreal Canada
probabilistic inference
machine learning
biologically plausible reinforcement learning
deep learning
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Tom Rainforth
University of Oxford United Kingdom
bayesian experimental design
statistical machine learning
deep learning
probabilistic programming
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Samir Maity
Aalborg University Denmark
machine learning
uncertainty quantification
predictive modeling
deep learning
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Federico Bocci
Radboud University Netherlands
machine learning
mathematical modeling
biophysical modeling
deep learning
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Daniel Roy
University of Toronto Canada
No active positions
machine learning
deep learning
probabilistic programming
statistical learning theory
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Trevor Campbell
University of British Columbia Canada
bayesian inference
bayesian theory
bayesian nonparametrics
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Edson Utazi
University of Southampton United Kingdom
bayesian computation
bayesian inference
machine learning
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