Keynote Speakers
Prof. Kun Yang(Personal
Page)
Member of Academia
Europaea (MAE)欧洲科学院院士,
IEEE Fellow, IET Fellow,
ACM Distinguished
Scientist
Chair Professor in the
School of Computer Science
& Electronic Engineering
Head of the Network
Convergence Laboratory
(NCL)
Nanjing
University, China /
University of Essex, UK
Speech Title:
AI-enabled Self-driving
Communication Networks
Abstract:
Modern Artificial
Intelligence (AI) has proven
to be a powerful enabler
that has gained success in
many vertical fields. There
is a clear evidence of
determined effort in the
communication and network
community to explore the AI
power to deliver 6G mobile
network’s promises of being
faster, greener and smarter.
This talk starts with a
brief introduction of 6G
mobile communication
systems, and then looks into
how new AI technologies, and
in particular machine
learning, come into play in
6G from different
perspectives. It covers new
trends in 6G communication
research such as data-driven
communication system design,
semantic communications,
digital twin networks (DTN),
and large model for wireless
networks. One major
objective of these
researches is to achieve
self-driving communication
networks where lengthy
standardization of such as
communication waveforms or
protocol design can be
somehow reduced or even
eliminated, thus enabling 6G
to self-drive to versatile
requirements from vertical
industries.
Bio: Kun
Yang received his PhD from
the Department of Electronic
& Electrical Engineering of
University College London
(UCL), UK. He is currently a
Chair Professor in the
School of Computer Science &
Electronic Engineering,
University of Essex, UK,
leading the Network
Convergence Laboratory
(NCL). He is also an
affiliated professor of
UESTC. His main research
interests include wireless
networks and communications,
future Internet and edge
computing. In particular he
is interested in energy
aspects of future
communication systems such
as 6G, promoting energy
self-sustainability via both
energy efficiency (green
communications and
networking) and energy
harvesting (wireless
charging). He has managed
research projects funded by
UK EPSRC, EU FP7/H2020, and
industries. He has published
400+ papers and filed 20
patents. He serves on the
editorial boards of a number
of IEEE journals (e.g., IEEE
ComMag, TNSE, WCL, TVT). He
is a Deputy Editor-in-Chief
of IET Smart Cities Journal.
He is a Distinguished
Lecturer of IEEE ComSoc. He
has been a Judge of GSMA
GLOMO Award at World Mobile
Congress – Barcelona since
2019. He is a Member of
Academia Europaea (MAE),
IEEE Fellow, IET Fellow, and
an ACM Distinguished
Scientist.
Prof. Tony Q.S. Quek(Personal
Page)
Fellow of Academy of
Engineering
Singapore新加坡工程院院士, IEEE
Fellow,
Cheng Tsang Man Chair
Professor
ST Engineering
Distinguished Professor
Director, Future Comms R&D
Programme
Head of ISTD Pillar
Singapore University of Technology and Design, Singapore
Speech Title:
Unlocking the Potential of
Federated Learning: A Path
towards Future Network
Intelligence
Abstract:
Machine learning,
particularly distributed
learning, stands as the
cornerstone in the vision of
future network intelligence,
owing to its remarkable
capability of addressing
intricate computational
tasks and modeling
complexities. In this talk,
we provide a comprehensive
coverage of a distributed
learning paradigm rooted in
federated learning.
Specifically, we start with
a brief overview of
federated learning. Then, we
elucidate an over-the-air
computation-based variant of
federated learning, which
circumvents the
communication bottleneck by
harnessing the superposition
properties of wireless
channels. Notably, such a
scheme presents new
advantages, such as reduced
processing latency and
enhanced privacy protection.
We also discuss several
approaches to personalize
the federated learning
framework by addressing
challenges stemming from
data heterogeneity. Lastly,
we share some of our recent
works investigating the
interplay between federated
learning and foundation
models.
Bio: Tony
Q.S. Quek received the B.E.
and M.E. degrees in
Electrical and Electronics
Engineering from Tokyo
Institute of Technology,
respectively. At
Massachusetts Institute of
Technology, he earned the
Ph.D. in Electrical
Engineering and Computer
Science. Currently, he is
the Cheng Tsang Man Chair
Professor with Singapore
University of Technology and
Design (SUTD) and ST
Engineering Distinguished
Professor. He also serves as
the Head of ISTD Pillar,
Director for Future
Communications R&D
Programme, Sector Lead for
SUTD AI Program, and the
Deputy Director of SUTD-ZJU
IDEA. His current research
topics include wireless
communications and
networking, 6G, network
intelligence,
non-terrestrial networks,
and open radio access
network.
Dr. Quek has been actively
involved in organizing and
chairing sessions and has
served as a TPC member in
numerous international
conferences. He is currently
serving as an Area Editor
for the IEEE Transactions on
Wireless Communications. He
was an Executive Editorial
Committee Member of the IEEE
Transactions on Wireless
Communications, an Editor of
the IEEE Transactions on
Communications, and an
Editor of the IEEE Wireless
Communications Letters.
Dr. Quek received the 2008
Philip Yeo Prize for
Outstanding Achievement in
Research, the 2012 IEEE
William R. Bennett Prize,
the 2016 IEEE Signal
Processing Society Young
Author Best Paper Award, the
2017 CTTC Early Achievement
Award, the 2017 IEEE ComSoc
AP Outstanding Paper Award,
the 2020 IEEE Communications
Society Young Author Best
Paper Award, the 2020 IEEE
Stephen O. Rice Prize, the
2020 Nokia Visiting
Professorship, the 2022 IEEE
Signal Processing Society
Best Paper Award, and the
2021-2023 World's Top 2%
Scientists. He is a Fellow
of IEEE and a Fellow of the
Academy of Engineering
Singapore.
Prof. Qingfu Zhang(Personal
Page)
IEEE Fellow
Chair Professor of Department of Computer Science
City University of Hong Kong, China
Speech Title: Multiobjective Evolutionary Computation based Decomposition
Abstract: Many optimization problems in the real world, by nature, have multiple conflicting objectives. Unlike a single optimization problem, multiobjective optimization problem has a set of Pareto optimal solutions (Pareto front) which are often required by a decision maker. Evolutionary algorithms are able to generate an approximation to the Pareto front in a single run, and many traditional optimization methods have been also developed for dealing with multiple objectives. Combination of evolutionary algorithms and traditional optimization methods should be a next generation multiobjective optimization solver. Decomposition techniques have been well used and studied in traditional multiobjective optimization. Over the last decade, a lot of effort has been devoted to build efficient multiobjective evolutionary algorithms based on decomposition (MOEA/D). In this talk, I will describe main ideas and techniques and some recent development in MOEA/D. I will also discuss some possible research issues in multiobjective evolutionary computation.
Bio: Qingfu Zhang is Chair Professor of Computational Intelligence at the Department of Computer Science, City University of Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics. MOEA/D, a multiobjective optimization algorithm developed by him and his students, is one of the two most used multiobjective optimization framework. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He has been in the list of SCI highly cited researchers for five consecutive years, from 2016 to 2020. He is an IEEE fellow.