Synthetic Data Calibration Professors in Global

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George Moustakides
University of Patras Greece
statistical signal processing
machine learning
sequential detection
adaptive signal processing
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Xiucai Ding
University of California, Davis United States of America
stochastic optimization
deep learning
scalable computation
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Dante Kalise
Imperial College London United Kingdom
computational optimisation
high-dimensional approximation
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Lisa Hellerstein
New York University Tandon School of Engineering United States of America
stochastic combinatorial optimization
approximation algorithms
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Vikram Krishnamurthy
Cornell University Netherlands
statistical signal processing
stochastic optimization
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Gordan Zitkovic
University of Texas at Austin United States of America
stochastic analysis
stochastic optimization
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Milad Lankarany
University of Toronto Canada
statistical signal processing
adaptive filtering
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Retsef Levi
Massachusetts Institute of Technology United States of America
approximation algorithms
stochastic optimization
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David Shmoys
Cornell University Netherlands
approximation algorithms
stochastic optimization
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Chaitanya Swamy
University of Waterloo Canada
approximation algorithms
stochastic optimization
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Chaitanya Swamy
University of Waterloo Canada
approximation algorithms
stochastic optimization
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Sreeraman Rajan
Carleton University Canada
statistical signal processing
machine learning
adaptive signal processing
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Oana Lang
Imperial College London United Kingdom
stochastic analysis
data assimilation
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Kevin Kamm
Umeå University Sweden
stochastic analysis
deep learning
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Mårten Wadenbäck
Linköping University Sweden
robust feature matching
machine learning
deep learning
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Yuxin Chen
Princeton University United States of America
statistical signal processing
machine learning
high-dimensional estimation
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Sahand Negahban
Yale University United States of America
statistical signal processing
machine learning
high-dimensional estimation
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Monika Eisenmann
Lund University Sweden
machine learning
stochastic numerical analysis
numerical approximation
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Luca Quadrifoglio
Texas A&M University United States of America
continuous approximation models
scheduling algorithms
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Ying Ma
Brown University United States of America
statistical optimization
deep learning
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Susan Hunter
Purdue University United States of America
stochastic optimization
simulation modeling
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