Outlier Detection Professors in Global

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Leman Akoglu
Carnegie Mellon University United States of America
outlier detection
anomaly detection
fraud detection
pattern discovery
machine learning
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Christian Maack
Aalborg University Denmark
anomaly detection
fault detection
machine learning
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Paula Branco
University of Ottawa Canada
anomaly detection
fraud detection
machine learning
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Paula Branco
University of Ottawa Canada
anomaly detection
fraud detection
machine learning
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Du Huynh
University of Western Australia Australia
object detection
pattern recognition
machine learning
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Guillermo Suarez De Tangil
King's College London United Kingdom
fraud detection
malware detection
machine learning
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Peter Melchior
Princeton University United States of America
outlier detection
machine learning
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Andries Van Der Ark
University of Amsterdam Netherlands
outlier detection
marginal modelling
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Sankaran Iyer
University of New South Wales Australia
anomaly detection
object detection
pedestrian detection
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Georgios Fellouris
University of Illinois United States of America
anomaly detection
sequential detection
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Pierre Tardif
University of Sherbrooke Canada
anomaly detection
intrusion detection
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Guofei Gu
Texas A&M University United States of America
anomaly detection
intrusion detection
botnet detection
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Erza Aminanto
Monash University Australia
anomaly detection
intrusion detection
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Nikos Tzavidis
University of Southampton United Kingdom
outlier robust inference
machine learning
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Ghita Berrada
London School of Economics and Political Science United Kingdom
anomaly detection
machine learning
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Randa Herzallah
University of Warwick United Kingdom
anomaly detection
machine learning
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Ghita Berrada
London School of Economics and Political Science United Kingdom
anomaly detection
machine learning
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Ligang He
University of Warwick United Kingdom
anomaly detection
machine learning
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Samuel Klein
University of Geneva Switzerland
anomaly detection
machine learning
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Guillaume Quétant
University of Geneva Switzerland
anomaly detection
machine learning
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Anis Assad Neto
University of Southern Denmark Denmark
anomaly detection
machine learning
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