High Dimensional Biostatistical Inference Professors in Global

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Kaixin Liu
London School of Economics and Political Science United Kingdom
high-dimensional time series
high-frequency time series
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Giulia Guidi
Cornell University Netherlands
large-scale computational biology
high-performance computing
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Tatjana Pavlenko
Uppsala University Sweden
high-dimensional problems
statistical inference
large-scale biomedical data
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Bahar Taskesen
University of Chicago Booth School of Business United States of America
statistical inference
large-scale stochastic optimization
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Moses Charikar
Stanford University United States of America
efficient algorithmic techniques for processing high-dimensional data sets
efficient algorithms for computational problems in high-dimensional statistics
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Fiona Panther
University of Western Australia Australia
statistical inference
high-performance computing
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Abhishek Chakrabortty
Texas A&M University United States of America
high-dimensional data
statistical inference
bayesian inference
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Moritz Schauer
Chalmers University of Technology Sweden
statistical inference
bayesian inference
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Torkel Erhardsson
Linköping University Sweden
statistical inference
bayesian inference
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Richard Border
Carnegie Mellon University United States of America
statistical inference
bayesian inference
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Jaehee Kim
Cornell University Netherlands
statistical inference
bayesian inference
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Alexander Wein
University of California, Davis United States of America
statistical inference
bayesian inference
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Nozomu Yachie
University of British Columbia Canada
high-resolution imaging
high-performance computing
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Jun Xie
Purdue University United States of America
statistical inference
bayesian bioinformatics
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Umberto Picchini
Chalmers University of Technology Sweden
statistical inference
stochastic modelling
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Umberto Picchini
University of Gothenburg Sweden
statistical inference
stochastic modelling
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Katharina Proksch
University of Twente Netherlands
statistical inference
statistical multiscale analysis
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Mert Sabuncu
Cornell University Netherlands
statistical inference
probabilistic modeling
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Bryon Aragam
University of Chicago Booth School of Business United States of America
deep generative models
probabilistic modeling
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Nan Zou
Macquarie University Australia
statistical inference for massive datasets
statistical inference for dynamical systems
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Eric Darve
Stanford University United States of America
high-performance computing
large-language models
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