Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系

RPG Seminar – Brain-inspired Random Memristors Pruning for Input-aware Dynamic SNN
2025-12-01 14:00 - 15:00 (Asia/Hong_Kong) | Online via Zoom
Zoom Link: https://hku.zoom.us/j/96497087839?pwd=[REDACTED]
Abstract
Machine learning has advanced unprecedentedly, exemplified by GPT-4 and SORA. However, they cannot parallel human brains in efficiency and adaptability due to differences in signal representation, optimization, run-time reconfigurability, and hardware architecture. To address these challenges, we introduce PRIME—a pruning optimization for input-aware dynamic memristive spiking neural networks. PRIME leverages spiking neurons to emulate biological spiking mechanisms and optimizes the topology of random memristive SNNs, mitigating memristor programming stochasticity. Additionally, it employs an input-aware early-stop policy to reduce latency and memristive in-memory computing to alleviate the von Neumann bottleneck. Validated on a memristor-based macro, PRIME achieves competitive classification accuracy and superior energy efficiency.
Speaker
Mr. Bo Wang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Bo Wang received B.Eng. degree in Power Engineering, Beihang University, Beijing, China, in 2020, and M.Eng. degree in Pattern Recognition and Intelligent Systems, Beihang University, Beijing, China, in 2022. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering under the supervision of Prof. Xiaojuan Qi. His research interests mainly include in-memory computing, Embodied AI and software-hardware co-design.
Organiser
Prof. Xiaojuan Qi
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Efficient Learning for Image Restoration and Single-Photon Imaging without Clean Data
2025-12-01 15:00 - 16:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/92646013468?pwd=[REDACTED]
Abstract
Supervised deep learning has revolutionized computational imaging but relies heavily on vast datasets of clean, ground-truth images, which are often challenging to acquire in practice. This seminar presents a series of methods that break this dependency by embracing weakly-supervised and unsupervised learning, directly addressing the challenge of learning without clean data. First, I will introduce a Fourier-based statistical equivalence between learning with noisy targets and clean targets. Building on this, I will present a weakly supervised framework for diverse image restoration tasks, along with two unsupervised denoising methods specifically designed for pixel-wise and stripe-wise noise. Finally, I will introduce a physics-informed unsupervised framework that can enable image restoration learning for single photon imaging with only the training data degraded by the blurring effect, Poisson noise, and readout noise. Collectively, this seminar demonstrates powerful and flexible learning paradigms that advance the computational imaging for scenarios where clean data is unavailable.
Speaker
Mr. Haosen Liu
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Haosen Liu received his B.Sc. and M.S. degrees from Huazhong University of Science and Technology, and is currently a fourth-year Ph.D. candidate in the Department of Electrical and Electronic Engineering at The University of Hong Kong under the supervision of Prof. Edmund Y. Lam. His research interests mainly include data-efficient deep learning methods for image restoration and computational imaging.
Organiser
Prof. Edmund Y. Lam
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Stretchable, Enhancement-mode PEDOT:PSS Organic Electrochemical Transistors
2025-12-02 10:00 - 11:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/97481664242
Abstract
The rise of wearable and implantable bioelectronics necessitates stretchable electronic devices and systems to seamlessly integrate with soft biological environments. Stretchable organic electrochemical transistors (OECTs), based on conducting polymer poly (3, 4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT: PSS), have emerged as a promising candidate because of their combined high stability and high transconductance. However, a stretchable, enhancement-mode PEDOT: PSS OECT (SE-OECT) is still missing, limiting the development of complementary and low-power integration systems. In this Letter, we report SE-OECTs. The devices showed typical enhancement-mode transistor behaviors with standby power as low as 0.1 μW while maintaining stable performance after 1000 cyclic tests within 50% strain.
Speaker
Miss Yan Wang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Yan Wang received her B.Sc in Chemistry from Nankai University. She is currently a Ph.D candidate in the WISE research group working on the processing of soft conducting polymers for high-performance soft OECTs.
Organiser
Prof. Shiming Zhang
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Enhancing Ultrasound Shear Wave Elasticity Imaging Through Spectral Methods and Optimized Sparse Arrays
2025-12-02 11:00 - 12:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Zoom Link: https://hku.zoom.us/j/97206601239?pwd=[REDACTED]
Abstract
Shear wave elasticity imaging (SWEI) is a widely-used technique for quantifying the stiffness of biological tissues. Tissue stiffness varies with pathological processes, for instance, local tissue stiffening due to increased stromal density in cancer. However, there still exist some challenges in SWEI. Specifically, on the one hand, the performance of current shear wave speed estimation methods still suffer from biased estimations or time-consuming computations, and are prone to wave distortions in in vivo cases. On the other hand, the 2-D nature of conventional SWEI leads to a lack of comprehensive analysis for 3-D shear wave propagation, for instance, in anisotropic tissues. Hence, for the first challenge, we have proposed a parameter-free, robust, and efficient group SWS estimation method coined as Fourier energy spectrum centroid (FESC). The proposed FESC method is based on the center of mass in ω − k space. It has been evaluated on data from computer simulations with additive Gaussian noise, a commercial elasticity phantom, an ex vivo pig liver, and in vivo biceps brachii muscles of three young healthy male subjects. The FESC method has been compared with four other benchmark methods. Statistical results showed that our FESC method exhibited excellent performance compared the other benchmark methods in terms of precision and computational efficiency. For the second challenge, due to the instantaneity of shear wave propagation and the adverse effect of high sidelobes on shear wave imaging. We initially have designed an on-grid quasi-flatten side-lobe (Q-Flats) 2D sparse array with 252 activated elements, which aims to achieve as high contrast performance as possible under the limits of resolution and maximum number of independent channels (i.e., 256). The imaging performance of the Q-Flats array has been evaluated using Field II simulations in a multi-angle steered diverging wave transmission scheme. It is demonstrated that the Q-Flats finds a good trade-off among resolution, contrast, and number of activated elements.
Speaker
Mr. Xi Zhang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Xi Zhang received the B.S. degree in Electrical Engineering and its Automation from Huazhong University of Science and Technology in 2017 and the Master degree in Biomedical engineering from Tsinghua university in 2020, respectively. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong, Hong Kong.
Organiser
Prof. Wei-Ning Lee
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

Seminar on Bi-Static Sensing for Next Generation Perceptive Communication Networks: Technologies and Applications
2025-12-02 14:00 - 15:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
The event time has been revised to start at 2:00 pm.
Abstract
Integrated Sensing and Communications (ISAC) represents a paradigm shift from conventional communication-only networks toward systems that natively integrate radar-like sensing capabilities. It has become a foundational technology for next-generation wireless systems, including Wi-Fi and 6G networks.
Bi-static sensing, where a sensing receiver exploits signals transmitted by another node, naturally aligns with the topology of communication networks. It circumvents the stringent full-duplex requirements of mono-static sensing and offers enhanced spatial sensing diversity. However, clock (Local oscillating signal) asynchronism, which inherently exists among spatially separated communication nodes, poses a central and challenging problem. It can cause ranging ambiguities and disrupt coherent processing of discontinuous measurements, such as those required for Doppler frequency estimation. If effectively resolved, sensing could be seamlessly realised within existing communication infrastructures, requiring minimal hardware or architectural modifications.
This talk explores advanced techniques for tackling clock asynchronism in bi-static sensing, with a focus on efficient single-receiver-based solutions. The problem will first be introduced in the context of 6G perceptive mobile networks, followed by a comprehensive overview of recent methods applicable to both multi-antenna and single-antenna configurations. I will then present our latest sensing applications developed using these techniques, including moving-object tracking, respiration and heartbeat monitoring, behavior recognition, and environmental sensing such as rainfall and water-level detection. The talk concludes by outlining key open challenges and future research directions in this rapidly evolving field.
Speaker
Prof. Andrew ZHANG
University of Technology Sydney
Speaker’s Biography
Prof. J. Andrew ZHANG (M’04-SM’11) is a Professor in the School of Electrical and Data Engineering, University of Technology Sydney, Australia. His research interests are in the area of signal processing for wireless communications and sensing. He has published more than 300 papers in leading Journals and conference proceedings, and has won 7 best paper awards. He is a recipient of CSIRO Chairman’s Medal and the Australian Engineering Innovation Award for exceptional research achievements in multi-gigabit wireless communications. He is one of the pioneer researchers in ISAC. He initiated the concept of perceptive mobile network in 2017. Since then, his team has published more than 70 top-tier journal papers on ISAC, including several highly cited and review articles. In this field, he has led or participated in multiple research projects with a total value of over AUD 8 million, established a Joint Laboratory on Network Sensing with a mobile network operator, developed multiple real-time ISAC demonstration systems, and is currently advancing their commercialisation. Prof. Zhang co-organised a number of ISAC workshops at leading conferences and special issues in leading IEEE journals. He has also delivered multiple ISAC tutorials and numerous keynotes and invited talks. For details, please refer to Prof. Zhang’s profile page: https://sites.google.com/view/andrewzhang
Organiser
Prof. Kaibin HUANG
Department of Electrical and Electronic Engineering,
The University of Hong Kong

All are welcome!

RPG Seminar – Ultrafast quantum sensing enabled by in-sensor computing
2025-12-02 14:00 - 15:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/96428082165?pwd=[REDACTED]
Abstract
Nitrogen Vacancy (NV) center, an optically addressable defect in diamond, has been explored as a promising sensing platform, due to its exceptional electronic spin properties at the room temperature. The widefield quantum sensing, leveraging this special property, allows for parallel readout of spatially resolved NV fluorescence, and therefore offers enormous potential in diverse fields, including temperature and magnetic field capturing. Conventional widefield quantum sensing method relying on traditional frame-based cameras, however, is usually limited in its sensing speed because it generates a massive amount of data in the form of image frames that needs to be transferred from the camera sensors for further processing.
This seminar will talk about a new method that realizes the ultrafast widefield quantum sensing by leveraging the bio-inspired in-sensor processing capability. The designed intelligent system mimics the working process of human eyes that merges signal detecting and processing together, and the resonance frequencies then can be extracted during the sensing period while no redundant raw data needs to be transferred outside, and thus an ultrashort sensing time (~ 10 µs in theory) can be achieved.
Speaker
Mr. Du Zhiyuan
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Du Zhiyuan received his B.S. and M.S. degree from the School of Optics and Photonics at Beijing Institute of Technology (BIT), China in 2016 and 2019, respectively. He is currently pursuing a Ph.D. degree at the Department of Electrical and Electronic Engineering under the supervision of Prof. Can Li. His research interests focus on in-sensor computing, emerging memory device development, and its application in intelligent quantum sensing.
Organiser
Prof. Can Li
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Data-Driven Intelligence and Energy-Aware Edge–Cloud Collaboration for IoT Systems
2025-12-03 11:00 - 12:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/92852691758?pwd=[REDACTED]
Abstract
The rapid growth of Internet-of-Things (IoT) deployments, combined with increasing adoption of edge computing, has created substantial challenges in energy efficiency, real-time intelligence, and collaborative resource management. IoT systems—ranging from smart buildings to cellular base stations—exhibit highly dynamic, heterogeneous, and energy-intensive behaviors that require new data-driven methods for prediction and optimization. This seminar investigates a unified edge–cloud collaborative framework that advances intelligent energy management across large-scale IoT environments. The first part presents HALO, a transformer-based HVAC load forecasting framework designed to address intrinsic complexities in real-world building operations. HALO incorporates adaptive preprocessing, a multi-scale local–global attention architecture, and a scale-fusion mechanism to handle data variability, multi-temporal fluctuations, and electronic sensor anomalies. Evaluations on six buildings with diverse climates and user patterns demonstrate that HALO significantly improves 24-hour load forecasting accuracy compared to state-of-the-art baselines. The second part introduces PATNet, an incentive-aware framework for energy–computation collaboration in mobile edge computing (MEC) networks. Leveraging an Overlapping Coalition Formation model, PATNet jointly optimizes MEC workload offloading, backup-battery utilization, and participation in real-world power adjustment (PA) incentive programs. Experiments using large-scale operational traces from up to 118,000 base stations show that PATNet increases utility significantly over existing strategies while maintaining service reliability. Together, HALO and PATNet demonstrate how data-driven prediction and energy-aware collaboration can be seamlessly integrated to enhance the intelligence, efficiency, and sustainability of next-generation IoT systems.
Speaker
Ms. Cheng Pan
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Cheng Pan is currently pursuing her Ph.D. in Electrical and Electronic Engineering at the University of Hong Kong under the supervision of Prof. Edith Ngai. She received her Master of Philosophy in Computer Science from the University of Hong Kong in 2023 and her Bachelor of Commerce degree in Management Information Systems from the University of Alberta in 2016. From 2016 to 2021, she worked as a data specialist in the healthcare industry in Canada. Her research interests include the Internet of Things and multimedia.
Organiser
Prof. Edith C. H. Ngai
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Direct Data-driven Control for Marine Vehicles
2025-12-03 14:00 - 15:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/91408893076?pwd=[REDACTED]
Abstract
Marine vehicles play an essential role in modern ocean operations, where reliable motion control is critical for safe, precise, and efficient task execution. This talk presents a direct data-driven control framework that synthesizes controllers directly from input–state–output data, thereby bypassing the need for complex hydrodynamic modeling and system identification. We address two fundamental motion control problems: autopilot (heading) control and trajectory tracking. For the autopilot problem, we design a linear state-feedback controller at each time step by solving a set of data-dependent linear matrix inequalities. The resulting controller guarantees internal stability together with a prescribed level of disturbance attenuation, and we further establish iterative feasibility of the underlying optimization problem, enabling real-time implementation. For the more challenging trajectory-tracking problem—which requires simultaneous regulation of heading and position and involves a kinetic subsystem with many unknown hydrodynamic parameters—we first derive a data-driven representation of the vessel kinetics. Building on this representation, we formulate a robust data-based optimization problem for controller synthesis that ensures global uniform ultimate boundedness of the closed-loop system.
Speaker
Mr. Jinjiang Li
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Jinjiang Li is a Ph.D. student in the Department of Electrical and Electronic Engineering. He received his B.E. degree and M.E. degree from Dalian Maritime University and Huazhong University of Science and Technology, respectively. His research focuses on data-driven control and motion control of robotics.
Organiser
Prof. Tao Liu
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Foldable Inverted Perovskite Solar Cells Enabled by Dual Strain Release
2025-12-03 16:30 - 17:30 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/8157366378?omn=98994435560
Abstract
The poor mechanical durability of perovskite films due to the severe intrinsic strain, and the brittle nature of the flexible ITO electrode hinder foldable perovskite solar cells (F-PSCs) realization. In this talk, the strategy of region-dependent microscopic and macroscopic strain suppression is demonstrated to achieve efficient F-PSCs on silver nanowires (AgNWs) electrodes. Fundamentally, by introducing the region-dependent modification approach of functionalized polymer incorporation, the significant release of microscopic strain in perovskite film is demonstrated by effectively suppressing defects at places with crystallization orientation variation of perovskite surface/grain boundaries. Equally important, the gradient macroscopic strain is simultaneously eliminated by inhibiting the FA+ (formamidinum) gradient distribution in perovskite film’s depth direction. The two-strain relaxations greatly enhance the mechanical durability of perovskite film, while also improving phase stability and suppressing ion migration. Finally, efficient F-PSCs (23% PCE) with remarkable foldability is realized.

Speaker
Mr. Biao Zhou
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
ZHOU Biao received the M.S. degree from Sichuan University in 2022. He is currently pursuing his Ph.D. degree under the guidance of Prof. Wallace C. H. Choy at the Department of Electrical and Electronic Engineering, the University of Hong Kong. His research interests include flexible photovoltaics, semiconductor thin films fabrication and characterization.
Organiser
Prof. Wallace C.H. Choy
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Lightweight Blockchain for Spatially and Temporally Scalable Federated Learning in Edge Networks
2025-12-04 09:30 - 10:30 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/97347520963?pwd=[REDACTED]
Abstract
Federated Learning (FL) has rapidly advanced as a foundational paradigm for realizing privacy-preserving intelligence in edge networks. However, its real-world deployment is fundamentally challenged by two dimensions: spatial scalability across a large, heterogeneous population of devices, and temporal robustness over long-lived, evolving learning processes. While blockchain technology offers inherent benefits like tamper-proof logging and decentralized trust, its naïve integration with FL is often intractable. This intractability stems from complex communication topologies, severe resource limitations, and the increasing cost of maintaining and retrieving an ever-expanding, shared knowledge base.
This seminar presents a unified view of lightweight blockchain designs systematically engineered to overcome these challenges. We introduce two novel systems: LiteChain and LiFeChain. LiteChain addresses spatial scalability in massive edge networks. Furthermore, it incorporates a Comprehensive Byzantine Fault Tolerance (CBFT) consensus and a secure update mechanism to reduce end-to-end latency, on-chain storage overhead. LiFeChain tackles the temporal dimension in Federated Lifelong Learning (FLL) for edge networks. It is combined with a Segmented Zero-knowledge Arbitration (Seg-ZA) protocol that enables efficient, bidirectional model verification with minimal on-chain disclosure. Implemented as a plug-and-play component in representative FLL frameworks, LiFeChain significantly enhances model robustness against long-term, cumulative attacks while sustaining efficiency and scalability.
These works demonstrate a systematic methodology for redesigning blockchain architectures to support FL that is simultaneously capable of scaling out in space and enduring over time within highly constrained edge networks.
Speaker
Miss Handi Chen
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Handi Chen received the B.E. degree in network engineering from Tianjin University of Since and Technology in 2019, and the M.E. degree in network engineering from the Dalian University of Technology in 2022. She is currently working toward the Ph.D. degree in Department of Electrical and Electronic Engineering, the University of Hong Kong. Her research interests include edge intelligence, mobile edge computing.
Organiser
Prof. Edith C.H. Ngai
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Planning and Operation Optimization of Electric-Coupled Systems for High-Speed Railways towards Flexibility and Resilience
2025-12-04 14:00 - 15:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/97338207102
Abstract
Electrified high-speed railways are emerging as major and spatially distributed electricity consumers in modern power systems, and their traction demand is tightly coupled with train dynamics and timetable scheduling. With the increasing exploitation of renewable resource endowments along railway corridors, electrified railways are evolving from pure loads into potential flexibility and resilience providers for low-carbon power systems. However, this evolution also brings new challenges and opportunities. On the one hand, existing models and operation strategies often treat high-speed railways as rigid electrical loads, leading to simplified kinetic-electrical representations and limited utilization of flexibility arising from coordinated train control and energy management of electric-coupled traction power supply systems. On the other hand, existing planning approaches for railway energy infrastructure remain largely economy-oriented and seldom incorporate explicit resilience criteria or the multi-stage couplings between energy supply adequacy and transportation service continuity. Hence, for the first challenge, we develop a unified kinetic-electrical coupling model together with a space-domain multi-phase pseudospectral coordination framework that jointly optimizes train trajectories and the operation of electric-coupled traction power supply systems with integrated photovoltaics and hybrid energy storage. The proposed method simultaneously accounts for detailed railway operating constraints and power system operating constraints within a numerically efficient optimal control formulation, demonstrating reduced traction energy cost, enhanced renewable utilization, and mitigated power fluctuations at the grid interface compared with benchmark strategies. For the second challenge, we propose a multi-stage resilience enhancement framework that integrates risk-aware capacity planning, rolling emergency energy management, and adaptive train control. A two-stage stochastic program with risk measurements is employed to co-optimize renewable, storage, and backup generation capacities under extreme grid outage and adverse weather scenarios, while operational layers coordinate distributed resources and train trajectories in real time. Case studies show that the proposed framework can substantially improve both energy supply resilience and transportation service robustness with moderate additional cost, highlighting electrified high-speed railways as promising flexibility and resilience resources in future power systems.

Speaker
Mr. Ruizhang Yang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Ruizhang Yang received his B.S. and M.S. degree in Electrical Engineering from Huazhong University of Science and Technology, China in 2017 and 2020, respectively. From 2020 to 2022, he worked as an Engineer at the Institute of Electrified Railway Design and Research, China Railway Siyuan Survey and Design Group. He is currently pursuing a Ph.D. degree at the Department of Electrical and Electronic Engineering at The University of Hong Kong, under the supervision of Prof. Yunhe Hou. His research interests focus on resilient transportation energy supply systems.
Organiser
Prof. Yunhe Hou
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – A Hybrid Iterative Framework for AC Unit Commitment: Integrating Global Linearization Updates with Local Constraint Corrections
2025-12-05 10:30 - 11:30 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/96462551842?pwd=[REDACTED]
Abstract
To address the computational challenges of AC Unit Commitment (AC-UC), this paper proposes a hybrid iterative framework that decomposes the MINLP model into a linearized MILP master problem and an exact AC feasibility check. The approach integrates Taylor-expansion-based linearization with a novel switching strategy that coordinates global updates for initial geometric alignment and local constraint corrections for subsequent stability. By freezing the Jacobian matrix after the initial phase, the method effectively mitigates integer oscillation. Case studies on IEEE standard test systems verify that the proposed method significantly reduces linearization errors, improves the quality of unit commitment decisions, minimizes physical violations and operating costs, and decreases the number of iterations required for convergence.
Speaker
Miss Miao Cheng
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Miao Cheng received her bachelor’s degree from Beihang University and her master’s degree from Tsinghua University, both in electrical and electronic engineering. She is currently working toward the Ph.D. degree in electrical and electronic engineering in the Department of Electrical and Electronic Engineering at the University of Hong Kong. Her current research interests include security-constrained unit commitment, inverter-based resources integration, non-convex optimization in power system.
Organiser
Prof. Yunhe Hou
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

Seminar on 40 Years of Proton Magnetic Resonance Spectroscopy in Human Brain
2025-12-05 11:00 - 12:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
The development of whole-body MRI scanners in the late 1980s at field strengths of 1.5T, together with other fundamental technological advances such shielded field gradients and single-shot spatial localization techniques, enabled the non-invasive collection of spectra from the human in just a few minutes of scan time. Since that time, there have been many technical advances and clinical studies performed, and it remains an active area of research and development. This presentation will review key technical developments including spatial localization techniques for both single voxel spectroscopy and spectroscopic imaging, spectral analysis, spectral editing, and the effects of increasing magnetic field strength. In addition, the metabolic information from in vivo MRS will be discussed, including metabolic changes that can be detected in various pathological states, and applications in the clinic. Finally, some of the challenges facing the clinical use of MRS and sustainability will be discussed.
Speaker
Prof. Peter BARKER
Director of Division of MR Research
John Hopkins University School of Medicine
Speaker’s Biography
Peter BARKER, D.Phil., is a Professor of Radiology and Oncology, and Director of the Division of MR Research at the Johns Hopkins University School of Medicine in Baltimore, Maryland. He holds a D.Phil. degree in Physical Chemistry from Oxford University. Since 1989, he has been a faculty member of the Russell H. Morgan Department of Radiology and Radiological Science at Johns Hopkins, where his primary interest has been the development of proton MR spectroscopy, and other MRI techniques, for applications in the human brain. He has published over 315 original, peer-reviewed articles, more than 45 commentaries, review articles and book chapters, as well as 3 books on Clinical MR Neuroimaging, Spectroscopy and Perfusion Imaging. Dr Barker is a fellow of the ISMRM society, and an editor for the journals Magnetic Resonance in Medicine and NMR in Biomedicine.
Organiser
Prof. Ed Xuekui WU
Chair of Biomedical Engineering,
Lam Woo Professorship in Biomedical Engineering
Department of Electrical and Electronic Engineering,
The University of Hong Kong

All are welcome!

Seminar on Semiconductor Nanodimer as a Partially Open Terahertz Resonator
2025-12-08 14:00 - 15:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
The event has been rescheduled to December 8, 2025 (Monday).
Abstract
Resonators are often the first apparatus to be constructed and thoroughly investigated when a new region of the spectrum is being explored. From the days of spark-gap generators in early radio transmission to the more recent maser and laser era, resonant systems have always been essential in enabling a given range of the spectrum to become accessible to electronic communication and instrument applications. With the current interest in terahertz technology, it would appear logical to search for structures or physical processes that exhibit natural resonances in the terahertz range. Plasma resonance in extrinsic semiconductors can be designed to exhibit field concentration and guiding characteristics that are impetus for sensing and circuitry applications for research and development of terahertz technology. While a single semiconductor nanoparticle (SNP) does exhibit surface plasmon resonance, the local terahertz field garnered near the two poles of an SNP lacks symmetry and is strongly influenced by the embedding medium. On the other hand, a semiconductor nanodimer (SND) formed by two SNPs with a gap in between them offers a more secluded environment for field enhancement with better symmetry in field distribution. Considerable attention has been given to metallic nanodimers, leading to their roles in sensing and antenna applications. On the other hand, investigations on SNP and SND are currently in the early stage. The salient characteristics of SNDs formed with matched and dissimilar SNPs are discussed in light of their potential for terahertz components and systems development.
Speaker
Prof. Thomas WONG
Professor Emeritus,
Department of Electrical and Computer Engineering,
Illinois Institute of Technology
Adjunct Professor of HKU-EEE
Speaker’s Biography
Thomas WONG received the B.Sc. degree from the University of Hong Kong, and the M.S. and Ph.D. degrees from Northwestern University, all degrees being in Electrical Engineering. He was a Product Engineer at Motorola Semiconductor (HK) before going to the United States for graduate study. He joined Illinois Institute of Technology as a faculty member in 1981 and is currently a Professor Emeritus in the Electrical and Computer Engineering (ECE) Department. He has conducted research in material measurements, charge transport in ionic and electronic conductors, transient electromagnetics, millimeter-wave communication systems, and propagation effects in high-speed semiconductor devices. In collaboration with Argonne National Laboratory and Fermilab, he has contributed to research in dielectric loaded accelerators, coupler design for superconducting multicell cavity resonators, and nanoscale position sensors. Recent activities have been on space-charge interactions in semiconductor nanostructures. He has served as Graduate Program Director and Department Chair of the ECE Department. In the 1998-1999 academic year he served as the Chair of the University Faculty Council. He is the author of Fundamentals of Distributed Amplification (Artech 1993) and coauthor of Electromagnetic Fields and Waves (Higher Education Press, 2002 and 2006). He is a Fellow of the International Association of Advanced Materials.
Organiser
Ir Dr. King Hang LAM
Department of Electrical and Electronic Engineering,
The University of Hong Kong

All are welcome!

RPG Seminar – Mamba model acceleration on RRAM-Based Compute-in-Memory (CIM) Systems integrated with Selective State-Space Streaming
2025-12-10 15:00 - 16:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/97793742616?pwd=[REDACTED]
Abstract
As Generative AI shifts toward handling massive context windows, the quadratic complexity of Transformer architecture has become a significant bottleneck. State Space Models (SSMs), particularly Mamba, have emerged as a promising solution, offering linear-time scaling and superior efficiency. However, the unique computational duality of SSMs—requiring both memory-intensive projections and agile, input-dependent state updates—presents new challenges that traditional von Neumann architectures and GPUs struggle to address efficiently.
This seminar explores the evolution of efficient sequence modeling and the critical hardware innovations required to support it. We will examine the “Memory Wall” problem in modern AI deployment and introduce Compute-in-Memory (CIM) using Resistive RAM (RRAM) as a paradigm shift to minimize data movement. The discussion will focus on the principles of hardware-software co-design, illustrating how tailored architecture can bridge the gap between memory-bound operations and dynamic recursions. By integrating specialized streaming dataflows with non-volatile memory technologies, we can define a new computational fabric capable of enabling the next generation of energy-efficient edge AI.
Speaker
Mr. Mingzi Li
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Mr. Mingzi Li is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering, supervised by Professor Han Wang. He received his B.Eng. in Computer Engineering from The Chinese University of Hong Kong in 2021 and the M.S. in Electrical and Electronic Engineering from The University of Hong Kong in 2022. His research interests include compute-in-memory architectures, RRAM-based systems, hardware acceleration for emerging sequence models and efficient AI systems.
Organiser
Prof. Han Wang
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

HKU-KAUST Joint Postgraduate Workshop on Computational Imaging 2025
2025-12-13 14:00 - 17:10 | Room 602, Student Commons 6/F, Pacific Plaza (Off-campus), Hong Kong SAR
All EEE postgraduate (TPg & RPg) students are welcome!
The upcoming “HKU-KAUST Joint Postgraduate Workshop on Computational Imaging 2025” will be held on December 13, 2025, organised by the Computational Imaging & Mixed Representation Laboratory. The workshop aims to encourage innovative spirit, promote excellence and sustain quality, strive for improvement, and connect communities. For details of the workshop and speakers, please visit the event website: https://hku.welight.fun/events/workshop_25Dec
Coffee, tea, and a reception will be provided.

MC
Prof. Evan Y. PENG, HKU EEE x CS
Coordinators
Dr. Xin Liu @ HKU; Dr. Qiang Fu @ KAUST
Speakers/Guests

Wolfgang HEIDRICH, King Abdullah University of Science and Technology & The University of Hong Kong
Yuhui LIU, The University of Hong Kong
Najia SHARMIN, The University of Hong Kong
Qiang FU, King Abdullah University of Science and Technology
Erqian DONG, The University of Hong Kong
Chutian WANG, The University of Hong Kong
Jiankai XING, Tsinghua University
Kaixuan WEI, King Abdullah University of Science and Technology
Zhenyang LI, The University of Hong Kong
Shi MAO, King Abdullah University of Science and Technology
Yanmin ZHU, The University of Hong Kong
Wenbin ZHOU, The University of Hong Kong

ACM SIGGRAPH Asia 2025 Pre-Conference Technical Workshop
2025-12-14 13:30 - 18:00 | Room CPD-2.42, 2/F, The Jockey Club Tower, Centennial Campus, HKU
Click HERE to view the details.

Seminar on Large Language Models (LLMs) in Space-Air-Ground Integrated Networks (SAGINs)
2025-12-15 15:00 - 16:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
*** Please note that the event has been rescheduled to Monday, December 15, 2025. ***
Abstract
Space-Air-Ground Integrated Networks (SAGINs), an architecture combining satellites, aerial platforms (like UAVs and High-Altitude Platforms), and terrestrial networks, aim to provide ubiquitous, high-speed, and seamless global coverage. Unmanned aerial vehicles (UAVs) have been widely deployed for reliable and energy-efficient data collection from spatially distributed devices, and hold great promise in supporting diverse Internet of Things (IoT) applications. Recently, Large Language Models (LLMs) have been used in SAGINs to enable more intelligent, adaptive, and autonomous networks, particularly in the context of 6G and beyond. In this talk, inspired by the remarkable generalisation and reasoning capabilities of large language models (LLMs), an LLM-based channel prediction framework, namely CPLLM, to forecast future channel state information (CSI) for LEO satellites based on historical CSI data will be presented. A large language model (LLM)-empowered critic-regularised decision transformer (DT) framework, termed LLM-CRDT, to learn effective UAV control policies will also be presented.
Speaker
Prof. Arumugam NALLANATHAN
School of Electronic Engineering and Computer Science,
Queen Mary University of London
Speaker’s Biography
Arumugam NALLANATHAN is Professor of Wireless Communications and the founding head of the Communication Systems Research (CSR) group in the School of Electronic Engineering and Computer Science at Queen Mary University of London since September 2017. He was with the Department of Informatics at King’s College London from December 2007 to August 2017, where he was Professor of Wireless Communications from April 2013 to August 2017. He was an Assistant Professor in the Department of Electrical and Computer Engineering, National University of Singapore from August 2000 to December 2007. His research interests include 6G Wireless Networks and Internet of Things (IoT). He published nearly 900 technical papers in scientific journals and international conferences. His publications have been cited over 36,000 times with an H-index of 95. He is a co-recipient of number of Best Paper Awards, including IEEE Communications Society Leonard G. Abraham Prize, 2022. He has been selected as a Web of Science (ISI) Highly Cited Researcher in 2016, 2022-2025. He is an IEEE Fellow and IEEE Distinguished Lecturer.
Organiser
Prof. Yuanwei LIU
Department of Electrical and Electronic Engineering,
The University of Hong Kong

All are welcome!

Seminar on Developing Value-driven AI: Building Large Language Models with Social Good Principles
2025-12-16 14:15 - 15:15 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
This talk highlights the critical need and urgency for academic communities to advance artificial intelligence (AI) with a focus on value-driven and socially-beneficial LLMs. The presentation is structured in two parts. First, I will briefly outline my academic and research background, our vision for AI for Social Good, and key contributions from over a decade of work in this field. The second part will focus on the development of a large language model (LLM) system embedded with social good principles. As LLMs, like ChatGPT, become integral to daily life, understanding and addressing their ethical and social implications is paramount. This talk explores how implicit values in AI systems can be identified and reshaped using techniques such as fine-tuning and data generation to align with inclusive, responsible, and ethical standards. By embedding societal values into LLM design, this work aims to foster AI systems that promote fairness, accountability, and positive societal impact. The significance of this talk lies in its potential to inspire HKU to prioritize ethical AI development, shaping a future where AI serves as an accelerator for social good.
Speaker
Prof. Jacqueline C.K. LAM
Associate Professor,
Department of Electrical and Electronic Engineering (EEE),
The University of Hong Kong (HKU)
Speaker’s Biography
Prof. Jacqueline C.K. LAM is an Associate Professor in the Department of Electrical and Electronic Engineering (EEE) at The University of Hong Kong (HKU), where she co-leads the HKU-AI to Advance Well-being and Society Research Lab. With a PhD in Environmental Management from HKU’s Faculty of Architecture (2008), she earned a competitive university-wide Research Assistant Professorship based in EEE, HKU in 2011, enabling her to pursue interdisciplinary research integrating data science, social sciences, neuroscience, and ethics. Prof. Lam champions AI for Social Good (AIfSG), her research places priority on addressing societal challenges, particularly in air pollution, asthma and Alzheimer’s disease, emphasizing fairness, explainability, through big data and AIfSG technologies.
Prof. Lam co-leads projects that secured four consecutive U.S. National Academy of Medicine Healthy Longevity Catalyst Awards (2021–2024) with Prof. Victor O.K. Li, advancing AI-driven early diagnosis and drug discovery for Alzheimer’s disease. She co-leads in Co-PI capacity a 50M HKD RGC Theme-based Research Grant for smart air pollution monitoring and health management, and a 3.25M HKD RGC-SPPR grant in 2011 for cross-border nuclear safety governance, reflecting her dedication to impactful, collaborative socially-beneficial research.
She cherishes her international collaborations, including roles as Visiting Senior Research Fellow at the University of Cambridge’s Judge Business School (since 2013), Visiting Fellow at Hughes Hall, and Visiting Academic at the Department of Computer Science and Technology at Cambridge. Prof. Lam a Visiting Scholar at MIT’s Centre for Energy and Environmental Policy Research and MIT EECS in 2019. In collaboration with Prof. Jon Crowcroft, FRS. At Cambridge, they have co-organized five AIfSG symposiums since 2018, fostering global academic dialogue in value-driven AI research.
In teaching, Prof. Lam is committed to mentoring PhD students at HKU, nurturing innovative thinkers in AIfSG. She co-established the pioneering HKU-Cambridge PhD Pathway, enabling engineering students to pursue an MPhil in Technology Policy at Cambridge Judge Business School, and pioneered interdisciplinary courses on Climate Change and Sustainability (2013–2020) and Deep Learning and Applications (2019-2025). As Area Editor of the Cambridge University Press journal Data and Policy, she contributes to global discussions on value-driven data policy. Her publications span multiple disciplines, including IEEE Transactions, Nature Scientific Reports, Nature Molecular Psychiatry, Journal of Alzheimer’s Disease, Environment International, Applied Energy, Energy Policy, and Data and Policy. Co-directing the HKU-AI WiSe and three HKU-Cambridge AI Research Platforms, Prof. Lam humbly seeks to advance AIfSG.
All are welcome!

Seminar on Efficient Generative Modelling, Multi-agent Systems Based on Knowledge Graphs and LLMs
2025-12-17 10:00 - 11:00 | Room CB-601J, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
I will overview our recent results on diffusion generative modelling and how to make inference faster, just in a few steps; also, I will provide some new concepts of Engineering AI and discuss how we can construct efficient multi-agent systems based on knowledge graphs and LLMs to solve complex engineering problems.
Speaker
Prof. Evgeny BURNAEV
Vice President for AI Development & Professor,
Skolkovo Institute of Science and Technology
Visiting Chair Professor,
Harbin Institute of Technology
Speaker’s Biography
Evgeny BURNAEV is Vice President for AI Development and Professor at the Skolkovo Institute of Science and Technology (Skoltech), where he also directs the Skoltech AI Center. His research focuses on engineering AI, generative modelling, optimal transport, physics-informed machine learning, and topological data analysis for reliable, efficient, and interpretable AI systems. At the AI Center, Burnaev leads interdisciplinary projects that bridge theoretical foundations and large-scale applications in energy, transport, materials, and climate modelling.
He has authored more than 200 peer-reviewed publications in leading international venues (NeurIPS, ICML, ICLR, IEEE, Nature Scientific Reports) and collaborates with global industry leaders such as Sber, Huawei, and Gazprom Neft. His achievements have been recognised with the Russian Government Prize in Science and Technology (2024), the Sber Science Award (2024), and inclusion in the Elsevier–Stanford global Top-2% scientists list (2023–2025). He also serves as Visiting Chair Professor at the Harbin Institute of Technology and contributes to international expert communities and program committees advancing transparent and trustworthy AI worldwide.
Organiser
Prof. Ngai WONG
Department of Electrical and Electronic Engineering,
The University of Hong Kong

All are welcome!

RPG Seminar – Bridging Visual Generation and Understanding in Native MLLMs with a Unified Visual Tokenizer
2025-12-17 11:00 - 12:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/98499142544?pwd=[REDACTED]
Abstract
The advent of GPT-4o highlights the immense potential of Multimodal Large Language Models (MLLMs) with native visual generation capabilities. These unified models offer precise control in multimodal interactions, enabling exceptional fluency in tasks such as multi-turn image editing and visual in-context learning. However, a fundamental dilemma remains in the choice of visual tokenizers for unified MLLMs – e.g., semantic tokenizers like CLIP excel in multimodal understanding but complicates generative modeling due to its high-dimensional, continuous feature space; Conversely, VQVAE tokenizers fit autoregressive generation but struggles to capture essential semantics for understanding.
This seminar explores how to design a unified visual tokenizer to bridge the gap in multimodal generation and understanding. Recent studies attempt to address this by connecting the training of VQVAE (for autoregressive generation) and CLIP (for understanding). However, directly combining these training objectives has been observed to cause severe loss conflicts. We will show that reconstruction and semantic supervision do not inherently conflict. Instead, the underlying bottleneck stems from limited representational capacity of discrete token space. Building on these insights, we introduce UniTok, a unified tokenizer featuring a novel multi-codebook quantization mechanism that effectively scales up the vocabulary size and bottleneck dimension.
Speaker
Mr. Chuofan Ma
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Mr. Chuofan Ma is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering, supervised by Professor Xiaojuan Qi. He received his bachelor’s degree in computer science from The University of Hong Kong in 2021. His research interests primarily lie in open-world visual intelligence and multi-modal foundation models.
Organiser
Prof. Xiaojuan Qi
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Towards End-to-End Visual Generation: from Matching to Evaluation
2025-12-19 11:00 - 12:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/91953486299?pwd=[REDACTED]
Abstract
This seminar examines how modern image generation models can be redesigned to achieve both high fidelity and high efficiency under flexible inference budgets. I will begin with a brief overview of generative modeling, focusing on diffusion models and related flow-based formulations. While these approaches deliver strong stability and visual quality, they typically rely on iterative reverse-time sampling and therefore require many sequential steps at inference, which limits practicality in latency- or compute-constrained settings.
Next, I will discuss mainstream strategies for reducing inference steps—such as distillation and consistency-style objectives—and highlight their common limitations, including reliance on strong pretrained teachers or difficulties in stable training from scratch at scale. Building on this, I will introduce an end-to-end perspective that unifies matching and evaluation within a single training framework. In particular, I will present the idea of self-evaluation as a training signal: the model learns from data-driven local supervision while simultaneously assessing its own generated samples using its current estimates, effectively acting as a dynamic self-teacher. This coupling bridges local learning and global distribution alignment, enabling any-step text-to-image inference that degrades gracefully with fewer steps and improves monotonically as more steps are allocated.
Speaker
Mr. Xin Yu
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Mr. Xin Yu is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering, supervised by Professor Xiaojuan Qi. He received his bachelor’s degree in Mathematics and Applied Mathematics from Sun Yat-sen University in 2021. His research interests primarily lie in generative models for computer vision.
Organiser
Prof. Xiaojuan Qi
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

Seminar on An Update on Machine Learning for Communication Networks
2025-12-19 15:00 - 16:30 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
The speaker aims to provide an update on recent progress by his research team on machine learning for communication networks. If time permits, he will also highlight his work on distributed quantum computing and quantum machine learning.
Efficient allocation of limited resources to competing demands is a crucial issue in the design and management of communication networks. In this seminar, the speaker will first introduce a new reinforcement-learning (RL) technique for achieving optimal resource allocation in networks with periodic traffic patterns. The effectiveness of this method will be demonstrated through numerical examples.
In addition, a new RL technique will be presented that separates representation learning from RL to enable fully decentralised learning in partially observable multi-agent settings. The approach relies on learned beliefs over the underlying system state. A belief model is first trained by using complete environment information, which is then used by a state-based RL algorithm using distributed, local observations only. A set of partially observable environments is constructed, and the efficacy of this new approach is shown and compared to relevant benchmarks.
If time permits, the speaker will also highlight his recent work on distributed quantum computing and quantum machine learning.
Speaker
Prof. Kin K. LEUNG
Department of Electrical and Electronic Engineering,
Department of Computing,
Imperial College, London
Speaker’s Biography
Kin K. LEUNG received his B.S. degree from the Chinese University of Hong Kong, and the M.S. and Ph.D. degrees from University of California, Los Angeles. He worked at AT&T Bell Labs and its successor companies in New Jersey from 1986 to 2004. Since then, he has been the Tanaka Chair Professor at Imperial College in London. He was the Head of Communications and Signal Processing Group from 2019 to 2024 and now serves as Co-Director of the School of Convergence Science: Space, Security and Telecommunications at Imperial. His current research focuses on optimisation and machine learning for design and control of large-scale communications, computer and quantum networks. He also works on multi-antenna and cross-layer designs for wireless networks.
He is a Fellow of the Royal Academy of Engineering, IEEE Fellow, IET Fellow, and member of Academia Europaea. He received the Distinguished Member of Technical Staff Award from AT&T Bell Labs (1994) and the Royal Society Wolfson Research Merits Award (2004-09). Jointly with his collaborators, he received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize (2021), the IEEE ComSoc Best Survey Paper Award (2022), the U.S.–UK Science and Technology Stocktake Award (2021), the Lanchester Prize Honorable Mention Award (1997), and several best conference paper awards. He chaired the IEEE Fellow Evaluation Committee for ComSoc (2012-15) and served as the General Chair of the IEEE INFOCOM 2025. He has served as an editor for 10 IEEE and ACM journals and chaired the Steering Committee for the IEEE Transactions on Mobile Computing. Currently, he is an editor for the ACM Computing Survey and International Journal of Sensor Networks.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Electronic Engineering,
The University of Hong Kong

All are welcome!

RPG Seminar – Design of High-Performance and Energy-Efficient AI Accelerator for Edge Computing
2025-12-23 14:30 - 15:30 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
This seminar provides a systematic showcase of our end-to-end energy-efficient solutions in AI accelerator design, spanning from the foundational compute unit to specialized architectures and large model deployment on edge devices. We begin by introducing a novel Reconfigurable Processing Element (PE), the core compute unit designed to support multiple floating-point (e.g., BF16, FP16) and fixed-point (e.g., INT8, INT4) Multiply-Accumulate (MAC) operations with 100% hardware utilization, achieving exceptional energy efficiency exceeding 1700 GFLOPS/W for deep learning tasks. Subsequently, addressing the need for extreme efficiency in edge vision, we present dedicated, multiplication-free Look-Up Table (LUT) accelerators like BDLUT (for blind denoising), EdgeLUT (for all fixed-resolution image restoration) and ScaleLUT (for real-time 4K super-resolution). These designs replace traditional convolutional operations with efficient LUT inference, resulting in significantly lower hardware and power consumption. Finally, we focus on high-efficiency acceleration for complex modern AI models: we propose the QuadINR framework, which utilizes hardware-efficient piecewise quadratic activation functions for Implicit Neural Representations (INR). For Large Language Models (LLMs), EdgeLLM is a highly efficient CPU-FPGA heterogeneous accelerator that employs a mixed-precision PE array (FP16/FP16 and FP16/INT4) and a unified data parallelism scheme, successfully tackling LLM deployment challenges to achieve up to 1.91x higher throughput and 7.55x better energy efficiency than commercial GPUs. In summary, this presentation offers a comprehensive view of our complete technological roadmap in the world of efficient AI chips, achieved through co-optimization of hardware and algorithms, to realize high-performance and energy-efficient AI acceleration for edge computing.
Speaker
Mr. Boyu Li
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Boyu Li is a Ph.D. candidate in the Department of Electrical and Electronic Engineering at The University of Hong Kong, under the supervision of Professor Ngai Wong. His research focuses on deep learning, reconfigurable computing, and the design of AI accelerators.
Organiser
Prof. Ngai Wong
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Design of Novel Structured Light Beams in Optical Manipulation and Two-Photon Microscopy
2025-12-23 14:30 - 15:30 | Online via Zoom
Zoom Link:
https://hku.zoom.us/j/6983793721?pwd=[REDACTED]
Abstract
Structured illumination refers to a class of optical imaging methods in which the sample is illuminated with a controlled spatial light pattern rather than uniform illumination. By altering the illumination pattern, it can facilitate the extraction of spatial or temporal information from the sample, and may improve imaging performance such as resolution or acquisition speed. It also enables precise manipulation of microscopic objects through targeted light patterns. One example is the higher-order BBs generated by adding a vortex phase to the zero-order BBs and exhibiting a ring-shaped intensity distribution. Their characteristic length results in different trapping states of particles with different sizes, which is of assistance to the size-sorting process. To this date, there has been no research on high-order conveyor beams with a vortex modulation. In this study, a spatial light modulator (SLM) is used to generate the ring-shaped conveyor beams. The beam profile is simulated and then confirmed with a two-photon microscopy system. The conveyor beam shows two alternating rings with an axial period. This technique opens a door to simultaneous optical manipulation of the particle’s OAM and longitudinal translation. The application of another class of beams, axially encoded beams, in multiphoton microscopy (MPM), will also be introduced. Combined with computational decoding, these beams enable axially parallel excitation (APEX) that extends per-plane exposure without slowing acquisition or requiring higher power. High-speed volumetric MPM with improved contrast will be demonstrated.
Speaker
Ms. Minghui SHI
Department of Electrical and Electronic Engineering,
The University of Hong Kong
Speaker’s Biography
Minghui SHI received the B.S. degree in optical information science and technology from the Beijing University of Technology, Beijing, China, in 2021. She is currently working toward the Ph.D. degree with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong. Her research interests include structured illumination, fluorescence imaging, and mid-infrared laser applications.
Organiser
Prof. Kenneth K.Y. WONG
Department of Electrical and Electronic Engineering, The University of Hong Kong

Seminar on Machine Learning, Artificial Intelligence, Neuro Imaging Focusing on Longevity and Dementia (MANIFOLD)
2026-01-06 11:00 - 12:00 | Tam Wing Fan Innovation Wing Two, G/F, Run Run Shaw Building, The University of Hong Kong
Abstract
Brain health is one of the key societal challenges for the 21st century, and much progress has been made in understanding and treating brain health conditions, aided by growing use of neuroimaging. Meanwhile, artificial intelligence and machine learning (AI/ML) technologies have revolutionised many domains, including healthcare. However, there is still a translational gap between AI/ML methods and the use of neuroimaging to detect, treat and care for people with neurodegenerative or neurodevelopmental conditions. My talk will provide an overview of the research of the MANIFOLD lab at UCL, that aims to bridge this gap and provide clinically useful neuroimaging tools to improve brain health. I will focus on methods that emphasise the individual patient, namely the brain-age paradigm and neuroanatomical normative modelling, applied to Alzheimer’s disease and dementia with Lewy bodies and frontotemporal dementia. Beyond this, I will talk about our research in explainable AI (XAI), AI/ML data fusion, automated ML and accessible MRI using portable scanners and how we have or plan to apply these in studies of brain diseases.
Speaker
Prof. James COLE
Professor of Neuroimage Computing,
UCL Hawkes Institute and the Dementia Research Centre (DRC),
University College London (UCL)
Speaker’s Biography
James Cole is Professor of Neuroimage Computing at the UCL Hawkes Institute and the Dementia Research Centre (DRC) at University College London (UCL). His research interests include brain ageing, neurological and psychiatric diseases, with a particular focus on ageing, neurodegeneration and dementia. His work uses machine learning, deep learning and related statistical methods with the goal of developing clinically useful neuroimaging tools. He is Principal Investigator of the MANIFOLD Lab.
Organiser
Prof. Ed Xuekui WU
Chair of Biomedical Engineering,
Lam Woo Professorship in Biomedical Engineering
Department of Electrical and Electronic Engineering,
The University of Hong Kong
Acknowledgement
Tam Wing Fan Innovation Wing Two

All are welcome!

Seminar on Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximisation
2026-01-06 14:30 - 15:30 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
In this talk, we will introduce SubLoRA, a rank determination method for Low-Rank Adaptation (LoRA) based on submodular function maximisation. In contrast to prior approaches, such as AdaLoRA, that rely on first-order (linearised) approximations of the loss function, SubLoRA utilises second-order information to capture the potentially complex loss landscape by incorporating the Hessian matrix. We show that the linearization becomes inaccurate and ill-conditioned when the LoRA parameters have been well optimised, motivating the need for a more reliable and nuanced second-order formulation. To this end, we reformulate the rank determination problem as a combinatorial optimisation problem with a quadratic objective. However, solving this problem exactly is NP-hard in general. To overcome the computational challenge, we introduce a submodular function maximisation framework and devise a greedy algorithm with approximation guarantees. We derive a sufficient and necessary condition under which the rank-determination objective becomes submodular, and construct a closed-form projection of the Hessian matrix that satisfies this condition while maintaining computational efficiency. Our method combines solid theoretical foundations, second-order accuracy, and practical computational efficiency. We further extend SubLoRA to a joint optimisation setting, alternating between LoRA parameter updates and rank determination under a rank budget constraint. Extensive experiments on fine-tuning physics-informed neural networks (PINNs) for solving partial differential equations (PDEs) demonstrate the effectiveness of our approach. Results show that SubLoRA outperforms existing methods in both rank determination and joint training performance.
Speaker
Dr. Yihang GAO
Department of Mathematics,
National University of Singapore (NUS), Singapore
Speaker’s Biography
Yihang GAO is currently a Research Fellow in the Department of Mathematics at the National University of Singapore (NUS), Singapore. He received the B.S. degree in Mathematics and Applied Mathematics from Zhejiang University, China, in 2020, and the Ph.D. degree in Mathematics from The University of Hong Kong (HKU), Hong Kong SAR, in 2024. His research interests include mathematical machine learning, optimisation, and scientific computing.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Electronic Engineering,
The University of Hong Kong
All are welcome!

RPG Seminar – Incentive Mechanism Design for Split Federated Learning
2026-01-07 14:00 - 15:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Split Federated Learning (SFL) combines computational efficiency with privacy preservation, but existing incentive mechanisms assume uniform cut-layer configurations across clients. This seminar presents a novel incentive mechanism that accounts for heterogeneous cut layer selections in SFL systems.
We formulate the problem as a Stackelberg game and derive theoretical convergence guarantees. The proposed mechanism provably achieves equilibrium with desirable strategic properties. Experimental results demonstrate significant improvements in participation rates, model accuracy, and resource efficiency compared to existing approaches.
Speaker
Ms. Xi Lin
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Ms. Xi Lin is an Mphil student in the Department of Electrical and Electronic Engineering at The University of Hong Kong, supervised by Prof. Xianhao Chen. She received her B.Eng. degree from Shandong University, China, in 2024. Her research interests include split federated learning, incentive mechanism design, and distributed machine learning systems.
Organiser
Prof. Xianhao Chen
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

Seminar on Quantum Dot Nanocrystal Based Optoelectronic Devices and Infrared Image Sensors
2026-01-14 14:00 - 15:00 | Tam Wing Fan Innovation Wing Two, G/F, Run Run Shaw Building, The University of Hong Kong
Abstract
Colloidal semiconductor quantum dot nanocrystals (QD NCs) have been shown to be promising materials for electronic and optoelectronic device applications because of their unique size dependent properties and solution processability. We develop a systematic methods to engineer the surface chemistry of quantum dot nanocrystals to control the charge carrier statistics as well as optical properties. We fabricate QD based infrared photodetectors (PDs) by examining and modifying the charge carrier transport and injection and by designing the structures of the nanocrystal based devices. Combinational studies have been conducted to improve the responsivity, linear dynamic range, noise, detectivity of the PDs. A new patterning method is introduced by engineering the surface states of QDs, achieving the high resolution devices. Newly developed patterning method is compatible with conventional photolithography process based on all‐solution processes. We introduce the pixel based and pixel-less image sensors with photomultiplication process in QD thin films. Finally, the strategy to incorporate various NCs and QDs into multi-functional devices and system is discussed.
Speaker
Prof. Soong Ju OH
Department of Materials Science and Engineering,
Korea University
Speaker’s Biography
Prof. Soong Ju OH obtained B.S. degree in Materials Science and Engineering from Korea University in 2007, and received his Ph.D degree in Materials Science and Engineering from the University of Pennsylvania. He worked in KIST from 2007 to 2008, and worked as a postdoctoral researcher at the University of Illinois at Urbana Champaign from 2014 to 2015. He joined the faculty of Korea University in 2015, and is now a full professor of Materials Science and Engineering at Korea University. His current research interests include quantum dot and nanocrystal based electronic and optoelectronic devices, and multifunctional, image and wearable sensors.
Organiser
Prof. Leo Tianshuo ZHAO
Department of Electrical and Electronic Engineering,
The University of Hong Kong

All are welcome!

Seminar on Advanced Solid-State Laser Technology and Its Applications
2026-01-20 10:00 - 11:30 | Room CB-601J, 6/F, Chow Yei Ching Building, The University of Hong Kong
*After the seminar, you are also welcome to attend in-person discussions with the speakers from 11:30 am to 12:30 pm in Room 601J.
Abstract
Solid-state lasers mean laser systems using solid-state materials as the gain medium, primarily including rod, slab, disc, fiber, and semiconductor lasers. These methods offer advantages such as high output power, excellent electro-optical efficiency, broad wavelength coverage, wide pulse modulation range, compact size, high stability and reliability, user-friendly operation, and low comprehensive cost. They have been widely applied in fields such as advanced manufacturing, electronics, biochemistry, medical applications, and scientific research, accounting for up to 84% of current laser equipment. This report will focus on the characteristics of high-power solid-state lasers, global research advancements, and the major achievements in high-power solid-state laser technology and applications made by the Laser Center at the Institute of Physics and Chemistry, Chinese Academy of Sciences (TIPC, CAS). The key achievements include ten-kilowatt-level near-infrared lasers, kilowatt-level green lasers, hundred-watt-level sodium beacon lasers, hundred-watt-level ultraviolet, and deep ultraviolet lasers, which could be applied in fields such as laser precision machining, detection imaging, advanced medical treatments, and cutting-edge scientific exploration.
Speaker
Prof. Yong BO & Prof. Xiaoyong GUO
Institute of Physics and Chemistry (TIPC),
Chinese Academy of Sciences (CAS)
Speakers’ Biography
Prof. Yong BO was born in Feb. 1972 and held Ph.D. in Engineering. He currently serves as a Research Fellow and Ph.D. Supervisor at the Institute of Physics and Chemistry (TIPC), Chinese Academy of Sciences (CAS). He obtained his Ph.D. from Tsinghua University in 2003 and worked at the Institute of Physics, CAS from 2003 to 2008. Since 2008, he has always been working at TIPC, CAS. His primary research focuses on high-power solid-state lasers with the frequency conversion technologies. He has achieved many world- advanced research results include ten-kilowatt-level near-infrared solid-state lasers, kilowatt-level green solid-state lasers, hundred-watt-level ultraviolet solid-state lasers, and hundred-watt-level sodium beacon solid-state lasers, which are applied in fields such as laser precision machining, detection imaging, and laser medicine. He has been awarded the Second Class Award of the National Technological Invention Award in 2017 and the Beijing Science and Technology Award in 2015. He has published over 200 papers and obtained more than 50 invention patents.
Prof. Xiaoyong GUO was born in Feb. 1974 and held a Ph.D. in Science. He currently serves as a Research Fellow, Ph.D. Supervisor, and Deputy Director at the Institute of Physics and Chemistry (TIPC), Chinese Academy of Sciences (CAS). Concurrently, He also holds positions as the Deputy Director of the National Key Laboratory of Light Turbine Power, head of the CAS Expert Group for Specialized Fields, and leader or member of multiple national-level expert groups. With extensive experience in optical and mass spectrometry research as well as strategic high-tech management, he has served as chief or deputy chief commander for numerous national initiatives and led multiple national and provincial-level research projects. His accolades include one national-level award and two provincial-and-ministerial-level awards. He has published dozens of academic papers and three monographs.
Organisers
Prof. Chao XIANG & Prof. Xianhao CHEN
Department of Electrical and Electronic Engineering,
The University of Hong Kong
