Choice Based Optimization Models Professors in Global

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Kalyanmoy Deb
Michigan State University United States of America
computational optimization
hybrid optimization algorithms
meta-modeling optimization
multi-objective optimization
process optimization
machine learning
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Vijaya Raghavendra Bollapragada
University of Texas at Austin United States of America
stochastic optimization
optimization algorithms
constrained optimization
distributed optimization
machine learning
nonlinear optimization
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Albert Berahas
University of Michigan United States of America
stochastic optimization
machine learning optimization
constrained optimization
distributed optimization
nonlinear optimization
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Eric Kerrigan
Imperial College London United Kingdom
control optimization
numerical optimization methods
model predictive control
multi-objective optimization
dynamic optimization
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Bartolomeo Stellato
Princeton University United States of America
mathematical optimization
differentiable optimization
large scale optimization
machine learning
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Cynthia Vinzant
University of Washington United States of America
mathematical optimization
combinatorial optimization
linear optimization
discrete optimization
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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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Reem Khir
Purdue University United States of America
robust stochastic optimization
constrained optimization
machine learning
optimization
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John Jørgensen
Technical University of Denmark Denmark
numerical optimization
model predictive control
process optimization
dynamic optimization
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Yinyu Ye
Stanford University United States of America
continuous optimization
machine learning
dynamic optimization
discrete optimization
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Bilge Atasoy
Delft University of Technology Netherlands
choice-based optimization models
prediction-based optimization
decision making models
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Ann-brith Strömberg
University of Gothenburg Sweden
mathematical optimization
simulation-based optimization
multi-objective optimization
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Michael Patriksson
University of Gothenburg Sweden
mathematical optimization
combinatorial optimization
nonlinear optimization
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Kiefer
Technical University of Berlin Germany
mathematical optimization
combinatorial optimization
scheduling models
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Marco Chiarandini
University of Southern Denmark Denmark
optimization algorithms
combinatorial optimization
continuous optimization theory
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Juergen Branke
Warwick Business School United Kingdom
simulation-based optimization
bayesian optimization
multi-objective optimization
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Ruiwei Jiang
University of Michigan United States of America
stochastic optimization
data-enabled stochastic optimization
discrete optimization
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Yaoliang Yu
University of Waterloo Canada
optimization algorithms
robust stochastic optimization
machine learning
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Arkadi Nemirovski
Georgia Institute of Technology United States of America
stochastic optimization
optimization theory
continuous optimization
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Mark Van Oyen
University of Michigan United States of America
stochastic optimization
online stochastic optimization
machine learning
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Aban Ansari-önnestam
Linköping University Sweden
mathematical optimization
continuous optimization
machine learning
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