Regularity Detection Professors in Global

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Leman Akoglu
Carnegie Mellon University United States of America
anomaly detection
pattern discovery
fraud detection
outlier detection
machine learning
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Sankaran Iyer
University of New South Wales Australia
anomaly detection
object detection
pedestrian detection
behavior analysis
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Panagiotis Charalampopoulos
King's College London United Kingdom
regularity detection
approximate pattern matching
sequence similarity
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Christian Maack
Aalborg University Denmark
anomaly detection
fault detection
machine learning
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Kim Blackmore
Australian National University Australia
pattern recognition
anomalous change detection
machine learning
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Du Huynh
University of Western Australia Australia
pattern recognition
object detection
machine learning
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Paula Branco
University of Ottawa Canada
anomaly detection
fraud detection
machine learning
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Maria Amer
Concordia University Canada
object detection
activity recognition
object tracking
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John Oommen
Carleton University Canada
pattern recognition
statistical pattern recognition
syntactic pattern recognition
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Marios Savvides
Carnegie Mellon University United States of America
object detection
face detection
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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Aijun Author
York University Canada
topic detection
pattern mining
machine learning
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J.c. Van Gemert
Delft University of Technology Netherlands
detection
pattern recognition
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G. Englebienne
University of Twente Netherlands
detection
activity recognition
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Pierre Tardif
University of Sherbrooke Canada
anomaly detection
intrusion detection
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Erza Aminanto
Monash University Australia
anomaly detection
intrusion detection
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Guofei Gu
Texas A&M University United States of America
anomaly detection
intrusion detection
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Fanzhen Liu
Macquarie University Australia
anomaly detection
machine learning
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Yue Zhao
University of Southern California United States of America
anomaly detection
out-of-distribution detection
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Eric Darve
Stanford University United States of America
anomaly detection
machine learning
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Anindya Das
Umeå University Sweden
anomaly detection
machine learning
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