Scalable Inference Professors in Global

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Rajesh Ranganath
New York University United States of America
statistical inference
probabilistic inference
causal inference
interpretability
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Dae Ham
University of Michigan United States of America
causal inference
adaptive sequential inference
randomization inference
design-based causal inference
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Ying Jin
University of Pennsylvania United States of America
selective inference
causal inference
distribution-free inference
generalizability
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Rina Foygel Barber
University of Chicago United States of America
statistical inference
shape-constrained inference
distribution-free inference
statistical estimation
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Purnamrita Sarkar
University of Texas at Austin United States of America
scalable inference algorithms
statistical machine learning
statistical models
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Yian Ma
University of California, San Diego United States of America
scalable inference methods
bayesian inference algorithms
sequential decision making
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Abhishek Chakrabortty
Texas A&M University United States of America
statistical inference
causal inference
bayesian inference
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Zoltán Szabó
London School of Economics and Political Science United Kingdom
scalable computation
bayesian inference
statistical machine learning
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Xinran Li
University of Chicago United States of America
causal inference
randomization-based inference
bayesian inference
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Nabarun Deb
University of Chicago Booth School of Business United States of America
statistical inference
nonparametric inference
statistical machine learning
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Saikat Chatterjee
Royal Institute of Technology Sweden
data inference
explainable machine learning
statistical machine learning
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Nabarun Deb
University of Chicago United States of America
causal inference
nonparametric inference
generative modeling
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Vanessa Mcnealis
University of Glasgow United Kingdom
causal inference
bayesian inference
hierarchical modelling
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Raaz Dwivedi
Cornell University Netherlands
causal inference
bayesian inference
statistical machine learning
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Christina Lee Yu
Cornell University Netherlands
inference networks
network causal inference
sequential decision making
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Line Clemmensen
University of Copenhagen Denmark
explainable modeling
statistical modeling
trustworthy modeling
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Filippo Ascolani
Duke University Singapore
nonparametric inference
bayesian inference
statistical modeling
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Denis Hirschfeldt
University of Chicago United States of America
computable model theory
computability theory
computable algebra
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Berk Gorgulu
McMaster University Canada
interpretable machine learning
stochastic modeling
predictive analytics
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Fei Wang
Cornell University Netherlands
predictive modeling
model interpretability
explainability
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Yexiang Xue
Purdue University United States of America
statistical modeling
probabilistic reasoning
computational sustainability
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