Keynote Speaker I
Prof. Xiaoli Li,
Nanyang Technological University, Singapore
Speech Title: Harnessing the Power of
AI: Transforming Industries Through Advanced Computer Science and
Engineering
Abstract: This
presentation delves into the transformative potential of computer
science and engineering across key industries, including manufacturing,
aerospace, professional services, and transportation. In manufacturing
and aerospace, AI-driven time series analytics emerge as a revolutionary
force, enabling predictive maintenance and condition monitoring.
Discover how these advancements optimize operations, minimize downtime,
and elevate productivity. In the professional services sector, AI proves
indispensable in enhancing auditor productivity, accurately predicting
staff attrition, and developing advanced Cyber Threat Hunting Tools to
bolster security. In the transportation industry, explore how AI can
optimize traffic light systems for increased efficiency. Join us on a
journey to uncover how computer science and engineering are reshaping
industries, driving innovation, and paving the way for real-world
transformation.
Biography: Dr. Xiaoli is currently a department head (Machine
Intellection department, consisting of 100+ AI and data scientists,
which is the largest AI and data science group in Singapore) and a
principal scientist at the Institute for Infocomm Research, A*STAR,
Singapore. He also holds adjunct professor position at Nanyang
Technological University (He was holding adjunct position at National
University of Singapore for 6 years). He is an IEEE Fellow and Fellow of
Asia-Pacific Artificial Intelligence Association (AAIA). Xiaoli is also
serving as KPMG-I2R joint lab co-director. He has been a member of
Information Technology Standards Committee (ITSC) from ESG Singapore and
Infocomm Media Development Authority (IMDA) since 2020. Moreover, he
serves as a health innovation expert panel member for the Ministry of
Health (MOH), expert panel member for Ministry of Education (MOE), as
well as an AI advisor for the Smart Nation and Digital Government Office
(SNDGO), Prime Minister s Office, highlighting his extensive involvement
in key Government and industry initiatives
Keynote Speaker II
Prof. Minghua Chen
City University of Hong Kong, Hong Kong, China
Speech Title: Synthesizing Distributed Algorithms for
Combinatorial Network Optimization
Abstract: Many important network design problems are
fundamentally combinatorial optimization problems. A large number of
such problems, however, cannot readily be tackled by distributed
algorithms. We develop a Markov approximation technique for synthesizing
distributed algorithms for network combinatorial problems with
near-optimal performance. We show that when using the log-sum-exp
function to approximate the optimal value of any combinatorial problem,
we end up with a solution that can be interpreted as the stationary
probability distribution of a class of time-reversible Markov chains.
Selected Markov chains among this class, or their carefully perturbed
versions, yield distributed algorithms that solve the log-sum-exp
approximated problem. The Markov Approximation technique allows one to
leverage the rich theories of Markov chains to design distributed
schemes with performance guarantees. By case studies, we illustrate that
it not only can provide fresh perspective to existing distributed
solutions, but also can help us generate new distributed algorithms in
other problem domains with provable performance, including cloud
computing, edge computing, and IoT scheduling.
Biography: Dr. Minghua Chen received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California Berkeley. He is currently a Professor of School of Data Science, City University of Hong Kong. He received the Eli Jury award from UC Berkeley (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and several best paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, ACM Multimedia Best Paper Award in 2012, IEEE INFOCOM Best Poster Award in 2021, and ACM e-Energy Best Paper Award in 2023. He is currently a Senior Editor for IEEE Systems Journal and an Executive Member of ACM SIGEnergy (as the Award Chair). His recent research interests include online optimization and algorithms, machine learning in power systems, intelligent transportation systems, distributed optimization, and delay-critical networked systems. He is an ACM Distinguished Scientist and an IEEE Fellow.
Invited Speaker I
Dr. Bruno Carpentieri,
Università di Salerno, Italy
Speech Title: Data Compression Does Not Only Compress Data
Abstract: Digital data compression is the coding of digital data
to minimize its representation. In compressed form digital data can be
stored more compactly and transmitted more rapidly. While at the
beginning data compression had as its main application the storage of
files on disks and digital memories today digital data compression is
the key protagonist in digital communication: we would not have, for
example, digital TV, smartphones and satellite communications and even
the AI engines without efficient data compression.Recent advances in
compression span a wide range of applications.
For example Internet and the World Wide Web infrastructures benefits
from compression, search engines can extend the idea of sketches that
work for text files, image, speech or music data, etc.. Additionally,
new general compression methods are always being developed, those that
allow indexing over compressed data or error resilience. Compression
also inspires information theoretic tools for pattern discovery and
classification, especially for bio sequences.
Today we know that data compression, data prediction, data
classification, learning and data mining are facets of the same
(multidimensional) coin.
In this talk we will review some of the recent advances in the field and
discover uncommon applications of data compression.
Biography: Bruno Carpentieri graduated in Computer Science at the
University of Salerno, and then obtained the Master of Arts Degree and
the Philosopy Doctorate Degree in Computer Science at the Brandeis
University (Waltham, MA, USA).Since 1991, he was first Researcher, then
Associate Professor and finally Full Professor of Computer Science at
the University of Salerno (Italy).His research interests include data
compression and information hiding.He was Associate Editor of IEEE Trans
magazine. on Image Processing and is still Associate Editor of the
international journals Algorithms and Security and Communication
Networks. He was also chair and organizer of various international
conferences including the International Conference on Data Compression,
Communication and Processing, co-chair of the International Conference
on Compression and Complexity of Sequences, and, for many years, a
member of the program committee of the IEEE Data Compression
Conference.He has been responsible for several European Commission
contracts in the field of data compression (compression of digital
images and videos).He directs the Data Compression Laboratory at the
Computer Science Department of the University of Salerno.
Dr. Sanghyuk Lee
New Uzbekistan University, Uzbekistan
Speech Title: Decision Making with Iterative Game with Semi-Perfect
Information
Abstract: A decision making framework with an iterative game
structure has been proposed. In the proposed structure, the maximum
benefit for each player is resolved by the iteration process. Based on
the payoff matrix, the optimal solution is sought by comparing it with
the criterion set by the players. To maximize the benefit for each party
(buyer and seller), an iterative game structure is proposed based on the
given payoff matrix and iterative machine. For real-world application,
practical example is considered, and a feasible solution is obtained. In
comparison with the existing body of research on game theory under
semi-perfect information, the provided solution is far from their payoff
but the result would be acceptable for two parties.
Biography: Sanghyuk Lee (M'21-SM'21) received Doctorate degree
from Seoul National University, Seoul, Korea, in Electrical Engineering
in 1998. His main research interests include data evaluation with
similarity measure, human signal analysis, high dimensional data
analysis, controller design for linear/nonlinear system, and observer
design for linear/nonlinear system. Dr. Lee is currently working as a
Professor at School of Computing of New Uzbekistan University, Tashkent,
Uzbekistan since 2023. He had been working as a founding director of the
Centre for Smart Grid and Information Convergence (CeSGIC) in Xi’an
Jiaotog-Liverpool University in Suzhou, China from 2014 to 2023. He also
had been serving as a Vice President of Korean Convergence Society (KCS)
from 2012 to 2019, and was appointed as an Adjunct Professor at Chiang
Mai University, Chiang Mai, Thailand, in 2016. Dr. Lee organized several
international conferences with KCS and was awarded multiple honors such
as outstanding scholar/best paper award from KCS and Korean Fuzzy
Society. Dr. Lee is a senior member of IEEE.
Dr. Amirrudin Kamsin
University Malaya, Malaysia
Speech Title: Challenges in Blended Learning
Abstract: Blended learning is widely regarded as an approach that
combines the benefits afforded by face-to-face and online learning
components. However, this approach of combining online with face-to-face
instructional components has raised concerns over the years. Several
studies have highlighted the overall challenges of blended learning mode
of instruction, but there has been no clear understanding of the
challenges that exist in the online component of blended learning. Thus,
a systematic review of literature was conducted with the aim of
identifying the challenges in the online component of blended learning
from students, teachers, and educational institutions perspectives.
Self-regulation challenges and challenges in using learning technology
are the key challenges that students face. Teachers’ challenges are
mainly on the use of technology for teaching. Challenges in the
provision of suitable instructional technology and effective training
support to teachers are the main challenges faced by educational
institutions. This review highlights the need for further investigations
to address students, teachers, and educational institutions’ challenges
in blended learning. In addition, we proposed some recommendations for
future research.
Biography: Amirrudin Kamsin is a Senior Lecturer at the Faculty
of Computer Science and Information Technology, and the Acting Director
and Deputy Director (ODL and Professional Programme) at the University
of Malaya Centre for Continuing Education (UMCCed), University of
Malaya, Malaysia. He received his BIT (Management) in 2001 and MSc in
Computer Animation in 2002 from University of Malaya and Bournemouth
University, UK respectively. He obtained his PhD in Computer Science
from University College London (UCL) in 2014. His research areas include
human-computer interaction (HCI), authentication systems, e-learning,
mobile applications, serious game, augmented reality and mobile health
services.
Speakers in 2025 to be announced soon......