Riemannian Optimization Professors in Global

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Kalyanmoy Deb
Michigan State University United States of America
computational optimization
hybrid optimization algorithms
multi-objective optimization
process optimization
meta-modeling optimization
evolutionary multi-objective optimization
machine learning
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Vijaya Raghavendra Bollapragada
University of Texas at Austin United States of America
stochastic optimization
optimization algorithms
nonlinear optimization
constrained optimization
distributed optimization
machine learning
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Albert Berahas
University of Michigan United States of America
stochastic optimization
machine learning optimization
nonlinear optimization
constrained optimization
distributed optimization
derivative-free optimization
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Bartolomeo Stellato
Princeton University United States of America
mathematical optimization
differentiable optimization
real-time embedded optimization
large scale optimization
machine learning
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Merve Bodur
University of Edinburgh United Kingdom
combinatorial optimization
inverse optimization
robust optimization
multiobjective optimization
machine learning
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Yaoliang Yu
University of Waterloo Canada
robust stochastic optimization
optimization algorithms
non-convex optimization
convex optimization
machine learning
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Katya Scheinberg
Georgia Institute of Technology United States of America
optimization
continuous optimization
derivative-free optimization
convex optimization
machine learning
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Jelena Diakonikolas
University of Wisconsin–Madison United States of America
large-scale optimization
first-order optimization algorithms
non-convex optimization
convex optimization
machine learning
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Cynthia Vinzant
University of Washington United States of America
mathematical optimization
linear optimization
combinatorial optimization
discrete optimization
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Shixuan Zhang
Texas A&M University United States of America
polynomial optimization
stochastic optimization
integer optimization
robust optimization
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Dick Den Hertog
University of Amsterdam Netherlands
linear optimization
nonlinear optimization
optimization in machine learning
robust optimization
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Michael Patriksson
University of Gothenburg Sweden
mathematical optimization
combinatorial optimization
nonlinear optimization
convex optimization
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Jeya Jeyakumar
University of New South Wales Australia
mathematical optimization
nonlinear optimization
robust optimization
machine learning
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Nathan Kallus
Cornell University Netherlands
stochastic optimization
optimization
robust optimization
machine learning
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Michael Patriksson
Chalmers University of Technology Sweden
combinatorial optimization
nonlinear optimization
non-differentiable optimization
convex optimization
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Phebe Vayanos
University of Southern California United States of America
stochastic optimization
integer optimization
robust optimization
machine learning
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Reem Khir
Purdue University United States of America
optimization
robust stochastic optimization
constrained optimization
machine learning
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John Chinneck
Carleton University Canada
optimization
nonlinear optimization
feasibility optimization
mixed integer optimization
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Nathaniel Bastian
Pennsylvania State University United States of America
optimization
computational stochastic optimization
multiple objective optimization
machine learning
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Mahdi Mehrtash
Johns Hopkins University United States of America
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stochastic optimization
global optimization
distributed optimization
power system optimization
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Eric Kerrigan
Imperial College London United Kingdom
numerical optimization methods
multi-objective optimization
dynamic optimization
control optimization
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