Metaheuristic Optimization Professors in Global

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Albert Berahas
University of Michigan United States of America
stochastic optimization
machine learning optimization
constrained optimization
nonlinear optimization
derivative-free optimization
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Brad Calder
University of California, San Diego United States of America
compiler optimizations
memory hierarchy placement optimizations
value-based optimization
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Eduardo Gildin
Texas A&M University United States of America
control optimization
production optimization
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Johan Ölvander
Linköping University Sweden
simulation optimization
design optimization
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Eric Kerrigan
Imperial College London United Kingdom
multi-objective optimization
dynamic optimization
control optimization
numerical optimization methods
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Vijaya Raghavendra Bollapragada
University of Texas at Austin United States of America
stochastic optimization
optimization algorithms
constrained optimization
nonlinear optimization
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Alexandre Jacquillat
Massachusetts Institute of Technology United States of America
stochastic optimization
large-scale optimization
integer optimization
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Clemens Thielen
Technical University of Munich Germany
discrete optimization
multiobjective parametric optimization
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Houra Mahmoudzadeh
University of Waterloo Canada
large-scale optimization
robust optimization
inverse optimization
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Jelena Diakonikolas
University of Wisconsin–Madison United States of America
convex optimization
non-convex optimization
large-scale optimization
first-order optimization algorithms
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Danial Davarnia
Purdue University United States of America
network optimization
discrete optimization
global optimization
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Marina Epelman
University of Michigan United States of America
mathematical optimization
discrete optimization
integer optimization
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Mark Daskin
University of Michigan United States of America
mathematical optimization
discrete optimization
integer optimization
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Bilge Atasoy
Delft University of Technology Netherlands
prediction-based optimization
choice-based optimization models
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Joerg Fliege
University of Southampton United Kingdom
discrete optimization
nonlinear optimization
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David Gutman
Texas A&M University United States of America
large-scale optimization
optimization manifolds
continuous optimization theory
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Ignacio Grossmann
Carnegie Mellon University United States of America
continuous optimization
discrete optimization
supply chain optimization
energy systems optimization
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Yaren Kaya
Columbia University United States of America
optimization
large-scale optimization
stochastic modeling
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Kaizheng Wang
Columbia University United States of America
optimization
large-scale optimization
stochastic modeling
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Borzou Rostami
University of Alberta Canada
large-scale optimization
integration of machine learning and optimization
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Charles Mader
University of Michigan United States of America
design optimization
multidisciplinary design optimization
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