KEYNOTE Speakers
Prof. Francis Chin (Fellow of IEEE, Fellow of
HKIE, Fellow of
HKACE, Emeritus Professor of University of Hong Kong, China)
University of Hong Kong, China
Speech Title: Development and Future of Deep
Learning
Abstract: Over the past decade, Deep Learning has
already demonstrated great success across many applications such as
object detection, image classification, speech recognition,
translation, summarization, and chatbots (LLMs and ChatGPT), text to
image and text to video. We envision that Deep Learning will have
great potential in many other areas of research and applications.
In this talk, we shall revisit the development of Deep Learning,
explain the key technologies for its success and how Deep Learning
works. Finally, we shall give insights on the future development of
Artificial General Intelligence.
Biography: Professor Francis
Chin has taught at University of Maryland Baltimore County,
University of Alberta, University of California San Diego, Chinese
University of Hong Kong, University of Texas at Dallas. Professor
Chin joined HKU in 1985, was founding Head and Chair of the
Department of Computer Science and Taikoo Professor of Engineering
at HKU. He had served as an Associate Dean of the Graduate School
from 2002 to 2006 and the Faculty of Engineering from 2007 to 2014.
Professor Chin has served as conference chairman and a member of the
program committee of numerous international workshops and
conferences. He was the Managing Editor of the International Journal
of Foundations of Computer Science and on the editorial boards of
journals. Professor Chin received the HKU Teaching Best Teaching,
Teaching Excellence Award and Outstanding Research Award in 1991,
2000 and 2010 respectively. Professor Chin with his bioinformatics
team has won the RECOMB 2022 Test-of-Time Award based on the impact
of their RECOMB2010 IDBA paper. He is also listed within the World’s
Top 2% Scientists published by Stanford University in October 2022.
Prof. Weisi Lin (Fellow of
IEEE, Fellow of IET, Highly Cited Researcher since 2019 by
Clarivate Analytics)
Nanyang Technological University, Singapore
Biography: Weisi Lin is an active researcher and
research leader in image processing, perception-based signal
modelling and assessment, video compression, and multimedia
communication. He had been the Lab Head, Visual Processing,
Institute for Infocomm Research (I2R), Singapore. He is currently a
President’s Chair Professor in College of Computing and Data
Science, Nanyang Technological University (NTU), Singapore, where he
also serves as the Associate Dean (Research). He is a Fellow of IEEE
and IET. He has been awarded Highly Cited Researcher since 2019 by
Clarivate Analytics, and elected for the Research Award 2023,
College of Engineering, NTU. He has been a Distinguished Lecturer in
both IEEE Circuits and Systems Society (2016-17) and Asia-Pacific
Signal and Information Processing Association (2012-13). He has been
an Associate Editor for IEEE Trans. Neural Networks Learn. Syst.,
IEEE Trans. Image Process., IEEE Trans. Circuits Syst. Video
Technol., IEEE Trans. Multim., IEEE Sig. Process. Lett., Quality and
User Experience, and J. Visual Commun. Image Represent. He serves as
a General Co-Chair for IEEE ICME 2025 and the Lead General Chair for
IEEE ICIP 2027, and has been a TP Chair for several international
conferences. He believes that good theory is practical and has
delivered 10+ major systems for industrial deployment with the
technology developed. He has been the Programme Lead for the Temasek
Foundation Programme for AI Research, Education & Innovation in
Asia, 2020-2024.
Prof. Ljiljana Trajkovic (Fellow of
IEEE)
Simon Fraser University, Canada
Speech Title: Machine Learning for Detecting Internet Traffic
Anomalies
Abstract: Collection and analysis of data from
deployed networks is essential for understanding communication
networks. Hence, data mining and statistical analysis of network
data have been employed to determine traffic loads, analyze patterns
of users' behavior, predict future network traffic, and detect
traffic anomalies. The Internet has historically been prone to
failures and attacks that significantly degrade its performance,
affect the Internet connectivity, and cause routing disconnections.
Frequent cases of various cyber threats have been encountered over
the years and, hence, detection of anomalous behavior is a topic of
great interest in cybersecurity. In described case studies, traffic
traces collected by various collection sites are used to classify
network anomalies. Various anomaly and intrusion detection
approaches based on machine learning have been employed to analyze
collected data. Deep learning, broad learning, gradient boosted
decision trees, and reservoir computing algorithms were used to
develop models based on collected datasets that contain Internet
worms, viruses, power outages, ransomware events, router
misconfigurations, Internet Protocol hijacks, and infrastructure
failures in times of conflict. The reported results indicate that
while performance of machine learning models greatly depends on the
used datasets, they are viable tools for detecting the Internet
anomalies.
Biography: Ljiljana Trajkovic received the Dipl.
Ing. degree from University of Pristina, Yugoslavia, the M.Sc.
degrees in electrical engineering and computer engineering from
Syracuse University, Syracuse, NY, and the Ph.D. degree in
electrical engineering from University of California at Los Angeles.
She is currently a professor in the School of Engineering Science,
Simon Fraser University, Burnaby, British Columbia, Canada. Her
research interests include communication networks and dynamical
systems. Dr. Trajkovic served as IEEE Division X Delegate/Director,
President of the IEEE Systems, Man, and Cybernetics Society, and
President of the IEEE Circuits and Systems Society. She serves as
Editor-in-Chief of the IEEE Transactions on Human-Machine Systems.
She was a Distinguished Lecturer of the IEEE Circuits and System
Society and a Distinguished Lecturer of the IEEE Systems, Man, and
Cybernetics Society. She is a Fellow of the IEEE.