Embedded Intelligence Professors in Global

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Phil Koopman
Carnegie Mellon University United States of America
embedded systems
embedded control networks
distributed embedded systems
embedded systems education
secure embedded systems
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Brokoslaw Laschowski
University of Toronto Canada
artificial intelligence
deep learning
neural networks
machine learning
brain-inspired algorithms
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Muhammad Shafique
New York University Tandon School of Engineering United States of America
embedded machine learning
embedded systems
artificial intelligence
brain-inspired computing
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Yiran Chen
Duke University Singapore
embedded systems
artificial intelligence
deep learning
machine learning
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Jingtao Yao
University of Regina Canada
artificial intelligence
web intelligence
neural networks
machine learning
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Risto Miikkulainen
University of Texas at Austin United States of America
artificial intelligence
evolutionary intelligence
neural networks
machine learning
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Johann Laux
University of Oxford United Kingdom
artificial intelligence
collective intelligence
inferential analytics
automated decision-making
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Vatsa Patel
Penn State Harrisburg United States of America
artificial intelligence
applied deep learning
intelligent systems
machine learning
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Kin-choong Yow
University of Regina Canada
artificial intelligence
deep learning
intelligent systems
machine learning
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Hugo Larochelle
University of Montreal Canada
artificial intelligence
deep learning
neural networks
machine learning
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Xiaofeng Liu
Yale University United States of America
artificial intelligence
deep learning
neural networks
computer-assisted
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Seyed Kia
Tilburg University Netherlands
artificial intelligence
deep learning
neural networks
machine learning
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Juergen Schmidhuber
Università della Svizzera italiana Switzerland
artificial intelligence
deep learning
neural networks
machine learning
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Daniel Ortiz Arroyo
Aalborg University Denmark
artificial intelligence
deep learning
machine learning
distributed computing
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Tahereh Koohi
University of New South Wales Australia
artificial intelligence
deep learning
neural networks
machine learning
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John Doe
University of Wisconsin–Madison United States of America
artificial intelligence
deep learning
neural networks
machine learning
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John Doe
KU Leuven Belgium
artificial intelligence
deep learning
neural networks
machine learning
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Pascal Vincent
University of Montreal Canada
artificial intelligence
deep learning
neural networks
machine learning
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Ally Smart
University of Western Australia Australia
intelligence
social intelligence
emotional intelligence
fluid intelligence
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Nathaniel Bastian
Pennsylvania State University United States of America
deep learning
intelligent systems
federated machine learning
machine learning
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Jingmin Huang
University of Southampton United Kingdom
deep learning
business intelligence
neural-inspired computing
machine learning
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