High Dimensional Sparse Methods Professors in Global

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Tengyao Wang
London School of Economics United Kingdom
sparse signal detection
high-dimensional statistics
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Tatjana Pavlenko
Uppsala University Sweden
sparse signal detection
high-dimensional problems
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Guillermo Sapiro
Duke University Singapore
sparse modeling
learning high dimensional data analysis
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Mahdi Cheraghchi
Imperial College London United Kingdom
high-dimensional geometry
sparse recovery
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Melba Crawford
Purdue University United States of America
high dimensional data analysis
multiresolution methods
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Kathryn Roeder
Carnegie Mellon University United States of America
high dimensional statistics
statistical methods
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Piotr Indyk
Massachusetts Institute of Technology United States of America
high-dimensional computational geometry
sparse recovery
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Rui Duan
Harvard T.H. Chan School of Public Health United States of America
high dimensional data analysis
statistical methods
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Marina Evangelou
Imperial College London United Kingdom
high-dimensional data analysis
statistical methods
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Lin Chen
University of Chicago United States of America
high-dimensional data analysis
statistical methods
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Jishnu Das
University of Pittsburgh United States of America
high-dimensional data analysis
statistical methods
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Richard Samworth
University of Cambridge United Kingdom
high-dimensional data analysis
statistical methods
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Anna Goldenberg
University of Toronto Canada
high-dimensional data analysis
statistical methods
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Paromita Dubey
University of Southern California United States of America
high dimensional data
statistical methods
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Sylvain Laizet
Imperial College London United Kingdom
high-order finite-difference methods
immersed boundary methods
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Sylvain Laizet
Imperial College London United Kingdom
high-order finite-difference methods
immersed boundary methods
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Professorship Biometry Molecular Medicine
Ludwig Maximilian University of Munich Germany
high-dimensional data
statistical methods
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Chad Schafer
Carnegie Mellon University United States of America
high-dimensional data
statistical methods
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Markus Pelger
Stanford University United States of America
high-dimensional data
statistical methods
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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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Marta Bryan
University of Toronto Canada
high-resolution spectroscopy
high-contrast imaging
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