Keynote Speakers

 

Prof. Xiaoli Li

IEEE Fellow, AAIA Fellow

Institute for Infocomm Research (I2R), A*STAR, Singapore

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.

 

Prof. Lei Meng

Shandong University, China

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.

 

Title：Modeling Semantic Space for Visual Analytics
Abstract: Visual surveillance becomes a key component in smart city applications, where images and videos captured from IoT cameras are analyzed the for downstream tasks, such as event detection and process tracking. Despite the recent advances in vision transformers and visual language models, redundancy-modeling and environmental-awareness are still open-challenges for visual representation learning. This talk will present the recent research outcomes published in AAAI'25 and IJCAI'25 from our lab on aligning visual content to the respective semantic factors, which focus on perceiving the semantic-level descriptive information and filtering event-irrelevant visual noises. The derived diffusion and causal models, our theoretical findings and the experimental verification will be depicted in details.

 

Dr. Chiang Liang Kok

Newcastle Australia Institute of Higher Education, Singapore

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.
 

Title: Biomedical Limb Lengthening Implant
Abstract: This presentation outlines the development and optimization of a Biomedical Limb Lengthening Implant with wireless integration, designed to address limb length discrepancies (LLD) affecting over 35% of adults. The implant combines an intramedullary nail with Bluetooth-enabled active feedback, enabling precise control via a patient-centric mobile app. Key innovations include a low-power PCB-integrated system (microcontroller, H-bridge, DC motor) for real-time adjustments and saline/muscle-simulated attenuation tests validating signal reliability (optimal ≤3m range). Finite Element Analysis (FEA) of thread designs (0.25–0.40mm pitch) identified Titanium Ti-6Al-4V as optimal, minimizing stress concentrations (≤0.40mm pitch) and displacement under torque (1.1 N·m), outperforming stainless steel. Clinical data analysis (1007 cases) highlighted mechanical failures (36% of complications), guiding design refinements to reduce risks like thread or distraction mechanism failure. Funded by Singapore’s NRF (S$200k), future work expands to IoT medical devices (e.g., drug infusion systems), with collaborations spanning orthopedic surgeons (Mount Elizabeth Hospital) and Medot Pte Ltd. This research bridges engineering precision with clinical needs, enhancing patient outcomes in limb reconstruction.

 

 

Anh Tuan Hoang

Institute for Infocomm Research, A*STAR, Singapore

Hoang Anh Tuan is a Principal Scientist at the Communications and Networks Department, Institute for Infocomm Research (I2R), A-Star, Singapore. He received his Ph.D. in Electrical Engineering from the National University of Singapore (NUS) in 2006, specializing in wireless communications.
Anh Tuan has contributed to a number of IEEE wireless standards, including IEEE 802.22 (TV White Space), 802.16.1a (high-reliability M2M, as Technical Editor), and 802.11ah (IoT). He received the IEEE Standards Association Certificates of Acknowledgement for significant technical contributions to both IEEE 802.16.1a and 802.22 standards.
Anh Tuan’s current research interests include Ambient IoT and digital twins. He also has extensive experience applying wireless technologies, e.g. V2X, to intelligent transportation systems for supporting autonomous vehicle testing, enhanced road safety, and AI-driven traffic control and management.

Title: Unlocking the Wireless Foundations of Industrial IoT
Abstract: The Industrial Internet of Things (IIoT) is no longer a vision — it’s a fast-evolving reality transforming industries from manufacturing to logistics, energy to smart cities. At the heart of this transformation lies a critical enabler: connectivity. But as IIoT use cases diversify in scale, complexity, and environment, so too must the wireless technologies that support them.
This keynote explores the broad spectrum of wireless technologies driving the future of IIoT — from long-range, infrastructure-grade solutions like NB-IoT and LoRaWAN, to facility-scale systems like Wi-Fi HaLow and BLE. These technologies form the backbone of today’s industrial automation, condition monitoring, and asset intelligence.
The talk will also spotlight an emerging paradigm: Ambient IoT — a radical shift toward battery-free, pervasive sensing powered by energy harvesting. With active standardization underway, Ambient IoT holds the promise of embedding intelligence into everyday objects at massive scale — redefining how we think about infrastructure, logistics, and system design.