Incentive Driven Algorithms Professors in Global

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Felix Dietrich
Technical University of Munich Germany
data-driven approximations
numerical algorithms
machine learning
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Alexandr Andoni
Columbia University United States of America
algorithmic foundations
sublinear algorithms
machine learning
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Olaf Schenk
Università della Svizzera italiana Switzerland
multicore algorithms
manycore algorithms
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Olaf Schenk
Università della Svizzera italiana Switzerland
multicore algorithms
manycore algorithms
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Nicole Wein
University of Michigan United States of America
dynamic algorithms
distributed algorithms
distance-estimation algorithms
online algorithms
parameterized algorithms
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Anne Trefethen
University of Oxford United Kingdom
numerical algorithms
energy-aware algorithms
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Eleni Drinea
Columbia University United States of America
algorithms for big data
randomized graph algorithms
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Eleni Drinea
Columbia University United States of America
algorithms for big data
randomized graph algorithms
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Renato Monteiro
Georgia Institute of Technology United States of America
complexity algorithms
computational optimization
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Aleksandra Korolova
University of Southern California United States of America
optimization algorithms
ad delivery algorithms
algorithmic fairness
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Parag Pendharkar
Penn State Harrisburg United States of America
genetic algorithms
inductive learning
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Scott Schmidler
Duke University Singapore
high-dimensional sampling algorithms
probabilistic machine learning
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Guido Maiello
University of Southampton United Kingdom
genetic algorithms
reward processing
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Oskar Eikenbroek
University of Twente Netherlands
genetic algorithms
mathematical optimization
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Sander Gribling
Tilburg University Netherlands
quantum algorithms
polynomial optimization
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Manolis Zampetakis
Yale University United States of America
sublinear algorithms
theoretical machine learning
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Manolis Zampetakis
Yale University United States of America
sublinear algorithms
theoretical machine learning
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Frank Wood
University of British Columbia Canada
probabilistic programming
deep generative modeling
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Rajesh Ranganath
New York University United States of America
machine learning
deep generative modeling
probabilistic inference
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Andy Ye
Toronto Metropolitan University Canada
graphics algorithms
digital communication algorithms
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Panos Parpas
Imperial College London United Kingdom
algorithms
computational optimization
mathematical optimization
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