Bayesian Optimisation Professors in Global

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Kalyanmoy Deb
Michigan State University United States of America
computational optimization
machine learning
multi-objective optimization
process optimization
evolutionary multi-objective optimization
hybrid optimization algorithms
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Vijaya Raghavendra Bollapragada
University of Texas at Austin United States of America
stochastic optimization
optimization algorithms
machine learning
constrained optimization
nonlinear optimization
distributed optimization
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Debdeep Pati
University of Wisconsin–Madison United States of America
efficient bayesian computation
bayesian methods
bayesian graphical models
approximate bayesian methods
machine learning
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Albert Berahas
University of Michigan United States of America
stochastic optimization
machine learning optimization
constrained optimization
nonlinear optimization
distributed optimization
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Merve Bodur
University of Edinburgh United Kingdom
combinatorial optimization
machine learning
multiobjective optimization
robust optimization
inverse optimization
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Yanan Fan
University of New South Wales Australia
bayesian computation
bayesian inference
approximate bayesian computation
bayesian nonparametrics
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Navish Kumar
University of Basel Switzerland
bayesian inference
riemannian optimization
machine learning
optimization
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Manuel Lopez-ibanez
University of Manchester United Kingdom
combinatorial optimisation
optimisation
multi-objective optimisation
machine learning
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Nathan Kallus
Cornell University Netherlands
stochastic optimization
machine learning
optimization
robust optimization
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Hina Shaheen
University of Manitoba Canada
bayesian inference
machine learning
computational modelling
statistical machine learning
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Nathaniel Bastian
Pennsylvania State University United States of America
computational stochastic optimization
machine learning
optimization
multiple objective optimization
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Shixuan Zhang
Texas A&M University United States of America
stochastic optimization
polynomial optimization
robust optimization
integer optimization
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Bartolomeo Stellato
Princeton University United States of America
mathematical optimization
machine learning
large scale optimization
differentiable optimization
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Jeya Jeyakumar
University of New South Wales Australia
mathematical optimization
machine learning
nonlinear optimization
robust optimization
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Phebe Vayanos
University of Southern California United States of America
stochastic optimization
machine learning
robust optimization
integer optimization
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Reem Khir
Purdue University United States of America
machine learning
optimization
robust stochastic optimization
constrained optimization
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Dick Den Hertog
University of Amsterdam Netherlands
linear optimization
optimization in machine learning
nonlinear optimization
robust optimization
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Jonathan Shapiro
University of Manchester United Kingdom
machine learning
optimization
probabilistic modeling
evolutionary algorithms
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Yaoliang Yu
University of Waterloo Canada
optimization algorithms
machine learning
robust stochastic optimization
non-convex optimization
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Jelena Diakonikolas
University of Wisconsin–Madison United States of America
large-scale optimization
machine learning
first-order optimization algorithms
non-convex optimization
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Eric Kerrigan
Imperial College London United Kingdom
multi-objective optimization
dynamic optimization
numerical optimization methods
control optimization
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