Li Xiaoli is the Department Head and a Senior Principal Scientist at the Institute for Infocomm Research, A*STAR, Singapore. He also holds an adjunct Full Professorship at the School of Computer Science and Engineering, Nanyang Technological University. His research spans AI, data mining, machine learning, and bioinformatics. With over 370 peer-reviewed publications and more than ten best paper awards, Xiaoli is widely recognized for his impactful contributions to the field. He serves as Editor-in-Chief of the Annual Review of Artificial Intelligence and as Associate Editor for top-tier journals including IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems. Xiaoli has also held leadership roles at premier conferences such as AAAI, IJCAI, NeurIPS, ICLR, KDD, and ICDM. Beyond academia, Xiaoli has led over 10 major R&D projects in collaboration with leading industry partners in aerospace, telecommunications, insurance, and professional services. He is an IEEE Fellow and a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA). Xiaoli has been named among the world’s top 2% scientists in AI by Stanford University and is a Clarivate Highly Cited Researcher.
Title: Recent Advances in AI for Sensor-Based Time Series Analytics
Abstract:The widespread deployment of sensors across
sectors such as manufacturing, aerospace, and healthcare has generated a
deluge of time series data, underscoring the urgent need for advanced
AI-driven analytics. This talk highlights recent breakthroughs in AI
techniques that empower predictive maintenance, machine health monitoring,
and operational optimization. We delve into three key areas of innovation:
(1) Self-supervised representation learning, which leverages contrastive
learning to extract meaningful features from unlabeled time series data; (2)
Unsupervised domain adaptation, which addresses both local and global
distribution shifts in multivariate sensor data to improve cross-domain
generalization; and (3) Model compression and optimization for edge AI,
enabling efficient deployment of AI models in resource-constrained
environments. We also explore the emerging role of foundation models for
time series analytics, and how they can be adapted to diverse downstream
applications. Together, these advances point toward a new era of scalable,
adaptable, and real-time AI for sensor-based systems.
Lei Meng is Professor with the School of Software, Shandong University. He received the B.Eng.’s degree in 2010 from Shandong University, China, and obtained the PhD’s degree in 2015 from Nanyang Technological University. From 2015 to 2020, he worked successively at Nanyang Technological University and National University of Singapore as Research Fellow and Senior Research Fellow. His research interests include multimedia computing, deep learning, and its applications in healthcare and digital twin for social governance. He has published a book with Springer and fifty conference and journal papers at top and renowned venues, such as MM, AAAI, TKDE and TNNLS. He serves as the Associate Editor of Applied Soft Computing, and the (senior) program committee member of top-tier conferences, such as MM, AAAI, IJCAI, and SIGIR.
In 2010, Chiang Liang graduated with First Class Honours
in Bachelor of Electrical & Electronic Engineering from Nanyang
Technological University (NTU). His exceptional performance earned him the
highly coveted Singapore EDB Integrated Circuit Design PhD Scholarship to
pursue his PhD at NTU. In 2014, Chiang Liang was awarded his PhD Degree in
Electrical & Electronic Engineering in which he delved deeply into power
management units, AI, sensors and energy harvesting systems. He also served
as an NTU undergraduate tutor and teaching assistant for NTU-TUM Master
courses. In 2014, Chiang Liang joined the Ministry of Défense, Mindef DSO
National Lab as a senior member of technical staff. Here, he spearheaded
several state-of-the-art projects, earning acclaim with the prestigious
Design Innovation Award (Individual) at the Electronics division level.
Chiang Liang was invited to be the Adjunct Professor (Faculty Member) at
Singapore University of Social Sciences (SUSS), where he teaches electronics
courses with passion. In 2021, Chiang Liang was bestowed with the
prestigious Gold Medal Award for Teaching Excellence (University level) at
SUSS.
In 2020, Chiang Liang joins the Newcastle Australia Institute of Higher
Education as a lecturer and program coordinator for the Bachelor of
Electrical and Electronic Engineering (BEEE). His influence extends far
beyond the classroom, as evidenced by his exclusive invitation to the
Channel News Asia (CNA) Money Mind programme in May 2021, where he shared
his expertise on blockchain technology and sustainable energy solutions. In
Nov 2021, he receives the Best Paper award at the 3rd ICESA. Chiang Liang
also serves as chairman for the STEM Industrial Advisory Board Committee and
a committee member for the PEI Exam Board Council. His expertise is sought
after on the international stage, with invitations as keynote/plenary
speaker and local organising chair for GMASC 2023, MSM 2024, CCCN 2024, ASET
2024, ACEE 2024 and PCDS 2024. Furthermore, he is in the technical program
committee for ICET 2024, ITET 2024, ICICDT 2024, TENCON 2024 and RASSE 2024.
He is also the chairperson and moderator for WES 2023, session chair for
AGBRP 2024, TENCON 2024 and ISCAS 2024. He also serves as the publicity
chair for MCSoC 2024 and RASSE 2024. Recently, he also serves as the special
session chair and co-trainer for workshop titled “Modern Technologies for
Sustainability and Asset Management” in McSOC 2024. In July 2024, he is
appointed to the Topical Advisory Panel for MDPI Electronics, Circuit and
Signal Processing Section. He also serves as guest editor and reviewer for
esteemed Q1/Q2 ranking journals such as MDPI Sensors/Electronics/Applied
Sciences, IEEE Access, Circuits, Systems, and Signal Processing and IEEE
Transactions on Industrial Electronics. Till date, he has been awarded
research funding of more than S$240K in both PI and co-PI capacity. With
over 50 publications in Q1/Q2 ranking journals, top conferences, and several
book chapters, Chiang Liang's scholarly impact continues to reverberate
across the global engineering landscape.
A. Lakshminarayanan (Lux) is a principal research engineer at I2R, working at the intersection of AI, Networking and Cybersecurity. In his 25+ year career as a practitioner and entrepreneur, he has delivered multiple projects scanning a wide swath of industry verticals. He is currently a part-time doctoral research student at Singapore Management University researching AI Engineering in the context of emerging AI regulations.
Title: Emerging AI Regulations – Implications for AI/ML in Networks
Abstract: AGI is just round the corner. AI/ML is getting adopted into every
aspect of digital infrastructure. At the same time awareness about AI safety
has also increased. AI/ML learn from data - data that might not be
representative or sufficient, data that might be noisy and biased. Powerful
black-box AI models are prone to hallucination and over-confidence. AI in
networking is particularly hard because networks are highly dynamic and
bespoke.
Globally, we have seen increasing regulatory activity, both horizontal e.g.
EU’s AI act and sector specific e.g. GSMA’s responsible AI maturity roadmap.
In this talk, I will give a bird’s eye view of AI regulatory activities from
around the world and how it is will impact AI in networking. I will also
cover 3GPP’s 5G-Advanced specifications on incorporating AI/ML and how this
can be accomplished in the context of AI regulations.