Machine Learning Optimization Professors in Global

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Kalyanmoy Deb
Michigan State University United States of America
computational optimization
process optimization
machine learning
multi-objective optimization
meta-modeling optimization
hybrid optimization algorithms
evolutionary multi-objective optimization
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Vijaya Raghavendra Bollapragada
University of Texas at Austin United States of America
optimization algorithms
machine learning
stochastic optimization
nonlinear optimization
distributed optimization
constrained optimization
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Bartolomeo Stellato
Princeton University United States of America
mathematical optimization
machine learning
large scale optimization
machine learning optimization data-driven algorithms
differentiable optimization
real-time embedded optimization
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Albert Berahas
University of Michigan United States of America
machine learning optimization
stochastic optimization
nonlinear optimization
distributed optimization
constrained optimization
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Merve Bodur
University of Edinburgh United Kingdom
machine learning
combinatorial optimization
multiobjective optimization
inverse optimization
robust optimization
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Nathaniel Bastian
Pennsylvania State University United States of America
optimization
machine learning
computational stochastic optimization
multiple objective optimization
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Dick Den Hertog
University of Amsterdam Netherlands
optimization in machine learning
linear optimization
nonlinear optimization
robust optimization
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Nathan Kallus
Cornell University Netherlands
optimization
machine learning
stochastic optimization
robust optimization
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Manuel Lopez-ibanez
University of Manchester United Kingdom
machine learning
multi-objective optimisation
combinatorial optimisation
optimisation
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Jeya Jeyakumar
University of New South Wales Australia
mathematical optimization
machine learning
nonlinear optimization
robust optimization
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Bissan Ghaddar
Western University Canada
optimization models
machine learning
transportation network optimization
energy optimization
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Jan Kronqvist
Royal Institute of Technology Sweden
optimization algorithms
ai in optimization
mixed-integer optimization
machine learning applications
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Phebe Vayanos
University of Southern California United States of America
machine learning
stochastic optimization
integer optimization
robust optimization
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Reem Khir
Purdue University United States of America
optimization
machine learning
robust stochastic optimization
constrained optimization
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Eric Kerrigan
Imperial College London United Kingdom
multi-objective optimization
control optimization
numerical optimization methods
dynamic optimization
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Chuan He
Linköping University Sweden
machine learning
large-scale optimization
continuous optimization
decentralized optimization
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Shixuan Zhang
Texas A&M University United States of America
stochastic optimization
polynomial optimization
integer optimization
robust optimization
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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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Yassine Yaakoubi
Concordia University Canada
machine learning-augmented optimization
supply value chain optimization
probabilistic machine learning
large-scale combinatorial optimization
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John Chinneck
Carleton University Canada
optimization
feasibility optimization
nonlinear optimization
mixed integer optimization
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Cynthia Vinzant
University of Washington United States of America
mathematical optimization
linear optimization
combinatorial optimization
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