Bayesian Spatial Modeling Professors in Global

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Jaime Ide
Yale University United States of America
predictive modeling
theoretical modeling
machine learning
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Ewen Harrison
University of Edinburgh United Kingdom
predictive modeling
bayesian approaches
machine learning
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Fangxin Fang
Imperial College London United Kingdom
predictive modeling
geophysical models
machine learning
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Jing Yao
University of Glasgow United Kingdom
spatial optimization
spatial statistics
location modeling
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Hao Zhang
Purdue University United States of America
spatial analysis
spatial statistics
machine learning
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David Matteson
Cornell University Netherlands
bayesian analysis
spatial analysis
spatial statistics
machine learning
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Dan Kowal
Cornell Bowers University United States of America
bayesian analysis
spatial analysis
spatial statistics
machine learning
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Somnath Ghosh
Johns Hopkins University United States of America
multiscale modeling
multi-physics modeling
machine learning
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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian graphical models
bayesian methods
hierarchical modeling
efficient bayesian computation
approximate bayesian methods
machine learning
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Simone Loreti
University of Bern Switzerland
mathematical modelling
spatial networks
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Marcial Gonzalez
Purdue University United States of America
predictive modeling
multi-scale modeling
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Hongyang Cheng
University of Twente Netherlands
bayesian uncertainty quantification
multi-scale modeling
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Carly Rozins
York University Canada
mathematical modelling
evolutionary modelling
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Mahmoud Alzoubi
Queen's University Canada
geomechanical modeling
numerical modeling
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Heather Graven
Imperial College London United Kingdom
biospheric modeling
atmospheric modeling
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Amy Burnicki
Pennsylvania State University United States of America
spatial data analysis
spatial statistics
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Juxin Liu
University of Saskatchewan Canada
spatial data analysis
spatial statistics
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Brandi Gaertner
Pennsylvania State University United States of America
spatial analysis
spatial statistics
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Adji Dieng
Princeton University United States of America
probabilistic modeling
statistical machine learning
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Bryon Aragam
University of Chicago Booth School of Business United States of America
probabilistic modeling
statistical machine learning
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Sergei Dudarev
University of Oxford United Kingdom
stochastic models
mathematical models
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