Assoc. Prof. Xie Ming
Nanyang Technological University, Singapore
Speech Title: KnowNet: A Large
Knowledge Model
Abstract: With the rise of Artificial
Intelligence, we are fortunate to witness
the transition from achieving machine’s
automation to achieving machine’s autonomy.
On one hand, the success of Artificial
Intelligence is guaranteed by the
availability of big data which is the result
of the formation of large systems that are
interconnected by various networks. On the
other hand, the importance of Artificial
Intelligence is due to the urgent demand for
self-intelligence by robots and machines of
tomorrow. Interestingly, the critical step
toward achieving machine’s self-intelligence
is the ability of designing large knowledge
models instead of improving existing
databases. In this invited talk, I will
share with the audience our research works
which aim at providing a general guiding
principle for the design of a large
knowledge model under the new paradigm of AI
3.0. The published research findings could
be found inside 1) Xie M., *Jayakumar K. S.
and *Chia H. F., 2004, Meaning-centric
Framework for Natural Text/Scene
Understanding by Robots, International
Journal of Humanoid Robotics, Vol. 1, No. 2,
pp. 375-407, and 2) Xie M., 2024, Top-down
Design of Human-like Teachable Mind, Special
Issue in Celebrating IJHR’s 20th Year
Anniversary, International Journal of
Humanoid Robotics.
Biography:
Xie Ming received the
B.Eng degree in control and automation
engineering from East-China Institute of
Textile Technology (now, under the name of
Donghua University, Shanghai, China).
Subsequently, as a recipient of the nation's
prestigious overseas scholarship of Chinese
government, he has completed the
postgraduate studies and doctorate research
works, and has received the Master degree
from the University of Valenciennes (France)
in 1986 as well as the PhD degree from the
University of Rennes (France) in 1989. Since
1986, he has worked as Research Assistant at
IRISA-INRIA Rennes, Expert Engineer at INRIA
Sophia-Antipolis, Lecturer/Senior
Lecturer/Associate Professor of Nanyang
Technological University, Fellow of
Singapore-MIT Alliance (SMA) (Affiliated
with Innovation in Manufacturing Systems and
Technology Program), Guest Professor of
Huazhong University of Science and
Technology (2002, 2006), Professor awarded
by China's Jiangsu Provincial Government
(2014), and Dean of College of Electrical
Engineering and Control Science at Nanjing
Tech University (2014-2016). He was the
General Chair of 2007 International
Conference on Climbing and Walking Robots
(CLAWAR), the General Chair of 2009
International Conference on Intelligent
Robotics and Applications (ICIRA), the
Co-founder of the International Journal of
Humanoid Robotics (SCI/SCIE indexed),
Co-founder of Singapore-China Association
for Advancement of Science and Technology,
Co-founder of Robotics Society of Singapore.
He has taught the courses such as Robotics,
Artificial Intelligence, Applied Machine
Vision, Measurement and Sensing Systems,
Microprocessor Systems, and University
Physics. In terms of scientific research, he
has authored three books in English, two
books in Chinese, and two edited books in
English. He has published several book
chapters, over 10 patents of invention, over
40 research papers in scientific journals
and over 100 research papers in
international conferences. He was the
recipient of one best conference paper award
from World Automation Congress, the
recipient of one best conference paper award
from CLAWAR, the recipient of one
outstanding paper award from International
Journal of Industrial Robot, the recipient
of one Gold Prize (S$8K) from CrayQuest, the
recipient of one Grand Champion Prize
(S$15K) from CrayQuest, the recipient of one
A-Star's Best Research Idea Prize (S$5K),
the recipient of one Silver Medal from
Dragon Design Foundation.
Prof. William Yeoh
Hong Kong Metropolitan University, Hong Kong SAR
Speech Title: Human-Centric
Cyber Security for Contemporary
Organisations
Abstract: In this talk, I will present my
research in the field of human-centric
cybersecurity, highlighting key milestones
and lessons learned throughout the journey.
My work focuses on the human and
organisational dimensions of cybersecurity,
covering areas such as zero-trust
cybersecurity maturity, critical success
factors for organisational cybersecurity,
blockchain applications in cybersecurity,
and simulated phishing attacks with embedded
training interventions. These studies,
published in leading peer-reviewed journals,
offer actionable insights for both academics
and practitioners. They provide valuable
implications for enhancing cybersecurity
culture, readiness, and resilience across
organisations. Drawing on this experience, I
will also discuss common challenges
encountered during the research process,
including interdisciplinary collaboration,
industry engagement, and navigating
publication pathways.
Biography:
William Yeoh is a full professor at the Lee
Shau Kee School of Business and
Administration, Hong Kong Metropolitan
University. Previously, he served at Deakin
University’s Deakin Business School, where
he was also the Innovation Head at Deakin
Cyber Research and Innovation Centre. His
research is supported by various funding
bodies and has been published in top-tier
journals. His research, teaching, and
service have been recognised with several
national and international awards, including
the Researcher of the Year Award from the
Australian Information Security Association
(AISA), the ICT Educator of the Year Gold
Award from the Australian Computer Society
(ACS), Australia’s Top 25 Analytics Leaders
award from the Institute of Analytics
Professionals of Australia (IAPA),
internationally competitive IBM Faculty
Awards, Deakin Faculty Research Excellence
Award, and Deakin Vice-Chancellor’s Award
for Value Innovation for his leadership of
the IBM Centre of Excellence in Business
Analytics. He is also an elected Fellow of
the Australian Computer Society.
Assoc. Prof. NORMA BT ALIAS FS
Universiti Kebangsaan Malaysia
Speech Title: From Black Box to Transparent Foundation: Theoretically-Grounded AI, Explainability, and Their Implementation on Advanced Computing Platforms
Abstract: The rapid evolution of Artificial
Intelligence (AI) is increasingly
underpinned by rigorous mathematical
foundations, drawing from optimization
theory, differential equations, and linear
algebra. However, the "black box" nature of
complex models creates a critical need for
Explainable AI (XAI) to ensure trust,
fairness, and accountability. This talk
explores the symbiotic relationship between
theoretically sound AI algorithms, the
principles of XAI, and the advanced
computing platforms, High-Performance
Computing (HPC), Cloud environments, and
quantum systems, that enable their scalable
and interpretable execution. This talk
explores the symbiotic relationship between
theoretically sound AI algorithms and the
advanced computing platforms, HPC, Cloud
environments, and nascent Quantum systems,
that enable their execution.
We begin by examining key algorithmic
classes, including optimization methods
grounded in convex analysis, neural
architectures inspired by kernel theory, and
tensor networks. We will highlight how their
inherent mathematical structures, such as
information-theoretic bounds and sensitivity
analysis, provide a natural foundation for
XAI, moving beyond post-hoc explanations to
intrinsically interpretable models.
The core of the presentation focuses on the
computational implementation. For HPC and
Cloud platforms, we analyze the
parallelization of large-scale linear
algebra operations and distributed training,
which are crucial for both training complex
models and efficiently computing XAI metrics
like Shapley values or integrated gradients.
We demonstrate how cloud-native
architectures facilitate robust, scalable
XAI pipelines for large-scale generative
models and deep learning systems ,
leveraging frameworks like PyTorch and
TensorFlow with MPI and GPU acceleration.
Finally, we venture into the frontier of
quantum computing, exploring how quantum
linear algebra algorithms could offer novel
pathways to understand model dynamics and
feature importance. The talk will conclude
with a forward-looking perspective on the
co-design of future AI systems, where
mathematical theory guides the creation of
not only more powerful but also inherently
more explainable and trustworthy models,
accelerated by the next generation of
computing architectures.
Biography:
Associate Professor Dr Norma Alias received
her Ph.D. in Industrial Computing
(Supercomputer) at Universiti Kebangsaan
Malaysia. She is the Committee of Synthetics
Biology RG and Future Ready Educator 4.0.
Handling 30 webinars per year at the local
and international levels. She is among 40
UTM research excellence in the year 2006,
Venus Distinguished Women Award and a mentor
for SUNSHINE++ program. She is chair for 4
international conferences, MSMK Chief Editor
and Associate Editorial Board for 38
international journals. Her research
encompasses big data analytics on
high-performance computing, validation of
complex mathematical model, solving
grandchallenge applications in
nanotechnology, loT, Al-smart digital
towards Industry 5.0
Assoc. Prof. Faisal Mahmuddin,
Hasanuddin University, Indonesia
Speech Title: Potential and Future of Ocean Wind Energy in Indonesia
Abstract:
Indonesia,
as the
world’s
largest
archipelagic
nation,
possesses
vast
untapped
potential
for
ocean-based
renewable
energy,
particularly
wind
energy.
With
over
80,000
kilometers
of
coastline
and
extensive
maritime
zones,
the
country
offers
promising
sites
for
offshore
and
nearshore
wind
energy
development.
This
presentation
explores
the
current
state,
potential,
and
future
direction
of ocean
wind
energy
in
Indonesia,
emphasizing
its
strategic
role in
achieving
national
energy
transition
goals
and
reducing
carbon
emissions.
Using
meteorological
and
oceanographic
data
analysis,
supported
by
computational
modeling
and
feasibility
studies,
the
research
identifies
high-potential
regions
for the
deployment
of
floating
wind
turbine
systems.
Key
challenges—including
technology
readiness,
infrastructure
limitations,
and
regulatory
frameworks—are
discussed
alongside
opportunities
for
local
innovation
and
international
collaboration.
The
study
concludes
that
with
proper
integration
of
marine
engineering
technology,
hybrid
renewable
systems,
and
policy
support,
ocean
wind
energy
could
become a
cornerstone
of
Indonesia’s
sustainable
blue
energy
future,
contributing
significantly
to
national
energy
security
and
regional
economic
development.
Biography:
Dr. Eng. Ir. Faisal Mahmuddin, ST.,
M.Inf.Tech., M.Eng. Head of Department of
Marine Engineering, Faculty of Engineering,
Hasanuddin University, Indonesia.
Dr. Faisal Mahmuddin is a senior lecturer
and researcher specializing in renewable
energy and marine engineering systems. He
earned his doctoral degree from Osaka
University, Japan, with extensive research
experience in ship hydrodynamics, ocean
renewable energy, and offshore technology.
Currently serving as Head of the Department
of Marine Engineering at Hasanuddin
University, he also leads numerous research
projects on floating wind turbines, hybrid
marine power systems, and sustainable vessel
design. Dr. Mahmuddin has published widely
in leading international journals and
conference proceedings, and serves as
Editor-in-Chief for several academic
journals, including the EPI International
Journal of Engineering. Through his
academic, industrial, and community
engagements, Dr. Mahmuddin contributes to
advancing green maritime innovation and the
application of renewable energy technologies
for sustainable ocean development.Faculty
Awards, Deakin Faculty Research Excellence
Award, and Deakin Vice-Chancellor’s Award
for Value Innovation for his leadership of
the IBM Centre of Excellence in Business
Analytics. He is also an elected Fellow of
the Australian Computer Society.