Stochastic Computing Professors in Global

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David Criens
Albert-Ludwigs-Universität Freiburg Germany
nonlinear stochastic processes
interacting stochastic processes
stochastic partial differential equations
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Mircea Grigoriu
Cornell University Netherlands
stochastic calculus
stochastic differential equations
numerical methods for solving stochastic problems
stochastic partial differential equations
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Dana Yang
Cornell Bowers University United States of America
statistical learning
computational statistics
mathematical modeling
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David Scheinker
Stanford University United States of America
probabilistic modeling
machine learning
mathematical optimization
statistical analysis
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Pui-kuen Yeung
Georgia Institute of Technology United States of America
computational algorithms
heterogeneous computing
massively parallel computation
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Ian Foster
University of Chicago United States of America
data-intensive computing
high-performance computing
distributed computing
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Maria Kateri
RWTH Aachen University Germany
stochastic modeling
applied stochastic processes
computational statistics
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Moritz Schauer
Chalmers University of Technology Sweden
dynamical stochastic models
stochastic differential equations
statistical inference
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Moritz Schauer
University of Gothenburg Sweden
dynamical stochastic models
stochastic differential equations
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Frederi Viens
Michigan State University United States of America
stochastic control
nonlinear stochastic filtering
stochastic volatility
stochastic pdes
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Kostas Kalogeropoulos
London School of Economics and Political Science United Kingdom
stochastic epidemic models
stochastic differential equations
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Kiefer
Technical University of Berlin Germany
stochastic online scheduling
mathematical optimization
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Hal Caswell
University of Amsterdam Netherlands
stochastic processes in demography
demographic stochasticity
environmental stochasticity
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Sahand Negahban
Yale University United States of America
statistical learning
statistical signal processing
machine learning
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Ying Ma
Brown University United States of America
statistical learning
statistical optimization
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Ansgar Steland
RWTH Aachen University Germany
stochastic processes
statistical learning
stochastics applications
machine learning
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Gareth Roberts
University of Warwick United Kingdom
stochastic simulation
stochastic processes
stochastic differential equations
computational statistics
statistical inference
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Gareth Roberts
University of Warwick United Kingdom
stochastic simulation
stochastic processes
stochastic differential equations
computational statistics
statistical inference
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Jiahua Chen
University of British Columbia Canada
statistical learning
computational statistics
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Berwin Turlach
University of Western Australia Australia
statistical computing
statistical learning
computational statistics
machine learning
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Ilker Yildirim
Yale University United States of America
statistical learning
computational statistics
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