Uniform Inference Professors in Global

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Rajesh Ranganath
New York University United States of America
statistical inference
probabilistic inference
causal inference
machine learning
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Abhishek Chakrabortty
Texas A&M University United States of America
statistical inference
causal inference
bayesian inference
machine learning
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Dae Ham
University of Michigan United States of America
causal inference
randomization inference
adaptive sequential inference
design-based causal inference
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Oliver Ray
University of Bristol United Kingdom
scientific inference
inductive reasoning
abductive reasoning
machine learning
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Victor Veitch
University of Chicago United States of America
statistical inference
causal inference
machine learning
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Mohammad Anam Khan
University of Toronto Canada
statistical inference
causal inference
machine learning
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Jordan Awan
Purdue University United States of America
statistical inference
simulation-based inference
machine learning
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Jaehee Kim
Cornell University Netherlands
statistical inference
bayesian inference
machine learning
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Sara Wade
University of Edinburgh United Kingdom
map inference
variational inference
machine learning
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Nikos Tzavidis
University of Southampton United Kingdom
statistical inference
outlier robust inference
machine learning
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Daniel Malinsky
Columbia University United States of America
causal inference
semiparametric inference
machine learning
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Ricardo Silva
University College London United Kingdom
relational inference
bayesian inference
machine learning
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Ricardo Silva
University College London United Kingdom
relational inference
bayesian inference
machine learning
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A.c.c. Coolen
Radboud University Netherlands
statistical inference
predictive analysis
machine learning
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Samrachana Adhikari
New York University United States of America
causal inference
predictive modeling
machine learning
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Thomas Icard
Stanford University United States of America
causal inference
probabilistic reasoning
machine learning
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Wendelin Böhmer
Delft University of Technology Netherlands
inductive reasoning
deductive reasoning
machine learning
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Ke Chen
University of Manchester United Kingdom
pattern recognition
machine perception
machine learning
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George Azzopardi
University of Groningen Netherlands
pattern recognition
predictive analysis
machine learning
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Jaime Ide
Yale University United States of America
pattern recognition
predictive modeling
machine learning
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Kin-choong Yow
University of Regina Canada
pattern recognition
commonsense reasoning
machine learning
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