Probabilistic Machine Learning Professors in Global

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Nikolay Malkin
University of Edinburgh United Kingdom
probabilistic reasoning
bayesian machine learning
machine reasoning
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Mats Granath
University of Gothenburg Sweden
deep machine learning
quantum machine learning
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Peter Grunewald
Heidelberg University Germany
predictive modelling
bayesian inference
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Chang-han Rhee
Northwestern University United States of America
machine learning theory
stochastic simulation
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Sebastiaan Terwijn
Radboud University Netherlands
probabilistic logic
computational learning theory
algorithmic randomness
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Edoardo Manino
University of Manchester United Kingdom
theoretical machine learning
bayesian machine learning
algorithmic game theory
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Huan Lei
Michigan State University United States of America
scientific machine learning
stochastic simulations
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Lisa Hellerstein
New York University Tandon School of Engineering United States of America
machine learning theory
stochastic combinatorial optimization
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Geoff Webb
Monash University Australia
machine learning
bayesian machine learning
ensemble machine learning
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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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Varun Kanade
University of Oxford United Kingdom
computational learning
randomised algorithms
deep learning
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Hayden Schaeffer
University of California Los Angeles United States of America
scientific machine learning
mathematical modeling
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Nikolas Nüsken
King's College London United Kingdom
computational bayesian inference
stochastic analysis
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Rasmus Kragh Jørgensen
Aalborg University Denmark
prognostic modeling
statistical modeling
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Kristian Debrabant
University of Southern Denmark Denmark
stochastic models
mathematical modeling
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Adnène Hajji
Laval University Canada
stochastic models
mathematical modeling
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Huan Lei
Michigan State University United States of America
stochastic simulations
scientific machine-learning
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Rutger Hermsen
Utrecht University Netherlands
computational models
mathematical modeling
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Peng Chen
Georgia Institute of Technology United States of America
scientific machine learning
bayesian inference
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John Oommen
Carleton University Canada
stochastic learning
statistical pattern recognition
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