SPEAKERS

Keynote Speakers (Alphabetize by Last Name)

Dr. Chiang Liang Kok Newcastle Australia Institute of Higher Education, Singapore

Chiang Liang Kok graduated with First Class Honours in Bachelor of Electrical & Electronic Engineering from the prestigious Nanyang Technological University (NTU) in 2010. His exceptional performance and potential were swiftly recognized as he was awarded the highly coveted Singapore Economic Development Board (EDB) Integrated Circuit Design PhD Scholarship to pursue his Doctor of Philosophy (PhD) at NTU. In 2014, Chiang Liang emerged triumphant, earning his PhD Degree in Electrical & Electronic Engineering.
Chiang Liang joined the Newcastle Australia Institute of Higher Education in 2020 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 November 2021, Chiang Liang's scholarly contributions were once again celebrated with the Best Paper award at the 4th International Conference on Environmental Science and Applications (ICESA 2021). As a respected leader in the engineering community, Chiang Liang plays an active role in shaping the future of the industry, serving 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 speaker and plenary speaker for GMASC 2023, MSM 2024, CCCN 2024 and ACEE 2024. He is also the local and technical co-chair for CCCN 2024, ACEE 2024 and PCDS 2024. Furthermore, he is in the technical program committee for ICET 2024, ITET 2024 and ICICDT 2024. He is also the chairperson and moderator for track 1 session in WES 2023 and session chair for AGBRP 2024. He also serves as the publicity chair for MCSoC 2024. Chiang Liang's dedication to scholarly excellence is evident in his role as guest editor and reviewer for esteemed journals such as MDPI Electronics Journal, IEEE Access, and Circuits, Systems, and Signal Processing. In early 2024, Chiang Liang's secured a S$20k external funding grant through a Memorandum of Understanding (MoU) with Cushman & Wakefield. With over 50 publications in Q2 ranking journals, top conferences, and several book chapters, Chiang Liang's scholarly impact continues to reverberate across the global engineering landscape, driving innovation and shaping the future of the industry. His dedication to mentoring future engineers is evident in his supervision of Final Year Project (FYP) students, guiding them in research endeavours spanning machine learning, biomedical devices, limb lengthening implants, and power management units for portable devices.
Speech 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.

Prof. Patrick P. C. Lee The Chinese University of Hong Kong, Hong Kong, China

Patrick P. C. Lee received the B.Eng. degree (first-class honors) in Information Engineering from the Chinese University of Hong Kong in 2001, the M.Phil. degree in Computer Science and Engineering from the Chinese University of Hong Kong in 2003, and the Ph.D. degree in Computer Science from Columbia University in 2008. He was a postdoctoral researcher at University of Massachusetts, Amherst in 2008-2009. He is now a Professor of the Department of Computer Science and Engineering at the Chinese University of Hong Kong. He currently heads the Applied Distributed Systems Lab and is working very closely with a group of graduate students on different systems projects. He was a collaborator with Alcatel-Lucent in developing network management solutions for 3G wireless networks in 2007-2011. His research interests are in various applied/systems topics on storage systems and computer networks, with dependability in mind.
Speech Title: Erasure Coding for Dependable Storage at Scale
Abstract: Ensuring the dependability of large-scale storage systems in the face of ever-growing data volumes is both critical and challenging. In this talk, I will present new theoretical and applied findings in erasure coding, a cost-effective redundancy technique for fault-tolerant storage. I will present general techniques and code constructions that accelerate the repair of storage failures and introduce a unified framework that enables the seamless deployment of diverse erasure coding solution in modern distributed storage systems.

Prof. Xin Luo Southwest University, China

Xin Luo (Fellow, IEEE) received the B.S. degree in computer science from the University of Electronic Science and Technology of China, Chengdu, China, in 2005, and the Ph.D. degree in computer science from the Beihang University, Beijing, China, in 2011. He is currently a Professor of Data Science and Computational Intelligence with the College of Computer and Information Science, Southwest University, Chongqing, China. He has authored or coauthored over 400 papers (including over 170 IEEE Transactions/Journal papers) in the areas of Artificial Intelligence and Data Science.
Speech Title: Nonstandard Tensor Networks
Abstract: Complex and temporal interactions among numerous nodes are frequently encountered in large-scale big data-related applications such as the recommender systems, social network service systems, and cryptocurrency network transaction systems. Such interactions data can be quantized into a step-N (N?3) tensor whose most entries are unknown, i.e., a nonstandard tensor. Despite its highly incompleteness, such a nonstandard tensor contains rich information regarding various desired patterns like the unknown interactions or undetected communities. To discover such patterns, this talk presents the latent factorization of tensors (LFT) models. An LFT model addresses the known data of the target nonstandard tensor in a data density-oriented way and establish highly efficient optimization algorithms for extracting desired latent features from it, thus implementing its representation learning accurately and efficiently. An LFT model has the great potential for industrial usage owing to its high efficiency in both computation and storage.

Assoc. Prof. Edith Cheuk Han NGAI The University of Hong Kong, Hong Kong, China

Edith C.H. Ngai is currently an Associate Professor in the Department of Electrical and Electronic Engineering at the University of Hong Kong. Before joining HKU in 2020, she was an Associate Professor in the Department of Information Technology at Uppsala University, Sweden. Her research interests include Internet-of-Things, edge intelligence, smart cities, and smart health. She was a VINNMER Fellow (2009) awarded by the Swedish Governmental Research Funding Agency VINNOVA. Her co-authored papers received a Best Paper Award in QShine 2023 and Best Paper Runner-Up Awards in ACM/IEEE IPSN 2013 and IEEE IWQoS 2010. She was an Area Editor of IEEE Internet of Things Journal from 2020 to 2022. She is currently an Associate Editor in IEEE Transactions of Mobile Computing, IEEE Transactions of Industrial Informatics, Ad Hoc Networks, and Computer Networks. She served as a program chair in ACM womENcourage 2015 and a TPC co-chair in IEEE SmartCity 2015, IEEE ISSNIP 2015, IEEE GreenCom 2022, and IEEE/ACM IWQoS 2024. She was a project leader of the “Green IoT” in Sweden, which was named on IVA’s 100-list by the Royal Swedish Academy of Engineering Sciences in 2020. She received a Meta Policy Research Award in Asia Pacific in 2022. She was selected as one of the N²Women Stars in Computer Networking and Communications in 2022. She is one of the HKU scholars in the top 1% worldwide by citations ranked by Clarivate Analytics in 2021-2023. She is an ACM Senior Member and an IEEE Senior Member. She is a Distinguished Lecturer in IEEE Communication Society in 2023-2024.
Speech Title: Adaptive Network Traffic Analysis for Intrusion Detection in Cyber-Physical Systems
Abstract: Cyber-Physical Systems (CPS) are systems that integrate the cyber and physical worlds, enabling computational intelligence and physical processes to interact through real-time sensing, actuation, and communication. They serve as the foundation of modern critical infrastructure, including industrial automation, transportation, energy systems, and healthcare. Securing CPS requires intrusion detection mechanisms that address the unique characteristics. In this talk, we focus on network traffic analysis, a fundamental task in defending against cyber intrusions. Network traffic carries rich, real-time information about system behavior, yet its non-stationarity and adversarial variability render static detection methods ineffective. To address this, we propose two adaptive network intrusion detection systems (NIDS) designed for ever-changing environments. First, we introduce AOC-IDS, an autonomous online intrusion detection framework featuring an anomaly detection module (ADM) with contrastive loss, and a labor-free online update mechanism. This self-supervised approach allows for continuous adaptation to dynamic network environments without human intervention. Secondly, we propose a strategic selection and forgetting (SSF) continual learning method for NIDS to tackle realistic constraints, including limited memory and labeling resources during system updates. SSF incorporates a strategic sample selection algorithm that prioritizes representative, drift-relevant data and a forgetting mechanism that discards outdated samples within a fixed memory buffer. This design effectively captures evolving patterns and ensures the model remains aligned with shifting data distributions, significantly enhancing adaptability to concept drift.

Invited Speakers (Alphabetize by Last Name)

Prof. Fumihiko Ino The University of Osaka, Japan

Fumihiko Ino received the B.E., M.E., and Ph.D. degrees in information and computer sciences from the University of Osaka, Osaka, Japan, in 1998, 2000, and 2004, respectively. He is currently a Professor in the Graduate School of Information Science and Technology at the University of Osaka. His research interests include parallel and distributed systems, software development tools, and performance evaluation.
Speech Title: Out-of-Core Techniques for Accelerating Large-Scale Stencil Computation on the GPU
Abstract: This talk focuses on out-of-core techniques for GPU-accelerated, large-scale stencil computation. We present a directive-based approach that employs several optimization techniques, including pipelined execution, temporal blocking, data reuse, and data compression. We also introduce an auto-tuning framework that empirically finds the best parameter values for performance acceleration.

Prof. Shuhao Zhang Huazhong University of Science and Technology, China

Shuhao Zhang is a Professor at Huazhong University of Science and Technology. His research spans streaming data processing, database systems, and high-performance parallel computing, with a current focus on building SAGE, a system platform for the large-model era that unifies execution and state management for agents, RAG systems, and long-running inference services. He previously served as an Assistant Professor at Nanyang Technological University (NTU) and was a Postdoctoral Researcher at Technische Universität Berlin, working on distributed data management. His research targets core challenges in vector search, semantic state maintenance, and multi-agent inference scheduling, with publications in SIGMOD, VLDB, ICDE, NeurIPS, and EMNLP, and several international patents. His work has been deployed in IoT systems, intelligent services, and smart manufacturing scenarios.
Speech Title:Building the Next-Generation Inference & Service Infrastructure for AIOS-Era Agent Applications
Abstract:Large models are transforming the application paradigm from single-shot model calls to agent-driven composite task flows, where future AI services operate as long-lived, adaptive intelligence rather than discrete apps. Supporting this shift requires a system foundation that offers a unified inference runtime, continuously evolving semantic state management, and consistent execution across devices and clusters.
SAGE addresses this need by introducing a system-level AI Application Runtime for next-generation AIOS. It decomposes inference into composable Inference Components, provides a dataflow-native execution model, and incorporates a programmable semantic memory layer with resource-aware scheduling and comprehensive observability. This enables agent applications to maintain stable logic and service quality across heterogeneous devices and large-scale compute environments.
This talk presents SAGE’s platform-oriented design principles and its system-level optimizations for agent collaboration, knowledge augmentation, and long-horizon inference services. We further discuss SAGE’s potential role as the intelligence runtime layer in future AIOS architectures, and outline upcoming work on semantic state scheduling, multi-device coordination, and optimizations for domestic hardware ecosystems.