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

RPG Seminar – A Novel Fabric Alignment System for Sewing
2025-11-26 15:00 - 16:00 (Asia/Hong_Kong) | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Zoom Link: https://hku.zoom.us/j/9706928305?omn=95929409545
Abstract
Accurate fabric alignment is essential in garment manufacturing, yet remains a challenging and labor-intensive task. This work presents a novel automated fabric alignment system that integrates a vision-guided robotic platform and a new Global Local Weighted Iterative Closest Point (GLW-ICP) algorithm. The system estimates the pose of wrinkle-free fabric panels—even under partial occlusion—by aligning global fabric edges and local sewing lines to CAD models. A roller-based end-effector then manipulates the fabric to achieve millimeter-level alignment accuracy. Unlike traditional ICP methods, GLW-ICP introduces adaptive weighting and sparsity to enhance robustness against occlusion and unmatched points. Experiments with various fabric types, including shirts and collars, demonstrate consistent, high-precision alignment. This system reduces operator dependency, improves consistency, and serves as a crucial step toward fully automated garment production workflows.

Speaker
Mr. Wenbo Dong
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Wenbo Dong received his B.Sc. in Automation from Northeastern University, China, and M.Sc. degrees in Control Engineering from Harbin Institute of Technology and Mechanical Engineering from the University of California, Riverside. He is currently pursuing a Ph.D. at the University of Hong Kong, where he is affiliated with the JC STEM Lab of Robotics for Soft Materials.
Organiser
Professor Kazuhiro Kosuge
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Parameter-sharing AI Model Caching, Delivery, and Inference at the Edge
2025-11-26 16:00 - 17:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/94531714904
Abstract
The rapid proliferation of AI applications in the 6G era calls for efficient support of edge intelligence, where models must be cached and executed at the network edge to deliver low-latency inference services. Unlike cloud data centers with abundant resources, edge servers are constrained in both storage and computation, creating new bottlenecks in AI service provisioning. Two critical yet underexplored challenges are storage efficiency in edge caching and model loading during edge inference, both of which fundamentally determine the efficiency of delivering AI services at the network edge.
This talk will present recent advances on parameter-sharing AI model edge caching and inference. We first introduce TrimCaching, a framework that leverages parameter sharing across models to improve storage efficiency in edge caching and significantly enhance model downloading performance. Building on this foundation, we then discuss PartialLoading, which reduces the dominant latency from repeatedly loading model parameters into GPU memory by strategically scheduling user requests to reuse shared parameters. Together, these two works establish a unified perspective on exploiting parameter sharing to mitigate both edge caching and inference bottlenecks, paving the way for scalable and efficient edge intelligence in next-generation networks.

Speaker
Mr. Guanqiao Qu
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Mr. Guanqiao Qu received his B.E. and M.E. degrees in Electronics and Information Engineering from Harbin Institute of Technology (HIT) in 2020 and 2022, respectively. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong (HKU). His research interests include edge intelligence, wireless networking, distributed learning, and edge inference.
Organiser
Prof. Xianhao Chen
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Split Learning: Empowering AI on Resource-Constrained Edge Devices
2025-11-26 17:00 - 18:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/94531714904
Abstract
The next-generation mobile network aims to natively support distributed intelligence, such as federated learning, across massive wireless edge devices. Unfortunately, in the era of large models, the deployment of federated learning faces significant obstacles due to the limited resources on edge devices. In this talk, I will briefly introduce split learning (SL) and elucidate how it overcomes resource limitations via device-server co-training, which transforms next-generation edge AI. Then, I will present our recent work on adaptive split federated learning (AdaptSFL) in resource-constrained edge networks. Specifically, our work first provides a unified convergence analysis of split federated learning (SFL) to quantify the impact of model splitting and client-side model aggregation on the learning performance, based on which the AdaptSFL framework is developed to adaptively control model splitting and client-side model aggregation to balance communication-computing latency and training convergence in SFL. Simulations results demonstrate the effectiveness of our approach in accelerating SFL under resource constraints. At last, I will conclude the talk by discussing open problems and challenges in SL at the wireless edge.

Speaker
Mr. Zheng Lin
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Zheng Lin is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China. His research interests include wireless networking, edge intelligence, and distributed machine learning.
Organiser
Prof. Xianhao Chen
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – A Continuous-Time Memristor-based Ising Solver for High-Efficiency Combinatorial Optimization
2025-11-27 14:00 - 15:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/99378601502?pwd=[REDACTED]
Abstract
Solving complex combinatorial optimization problems is a fundamental challenge that pushes conventional digital computers to their limits. While some physics-based computing approaches offer a promising alternative, many existing systems remain trapped in a hybrid digital-analog loop, burdened by slow, power-hungry iterations and data conversions.
This work presents a fully integrated memristor-based Ising machine chip that operates as a fully analog dynamic system, solving these problems in a single shot. Its architecture embeds the entire optimization process into the continuous physical dynamics of the circuit. By encoding the problem’s couplings as memristor conductances, the hardware directly minimizes the system’s Hamiltonian through a single, continuous analog transient.
Experimental results from a 96-spin integrated chip demonstrate the system’s capability to find high-quality solutions using a quantum-inspired annealing protocol. By eliminating digital overhead entirely, the solver achieves a nearly 10x improvement in energy efficiency and a significant speed-up. This approach opens a new avenue for creating powerful and scalable hardware accelerators for the next generation of computing.
Speaker
Ms. Keyi Shan
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Keyi Shan is a Ph.D. student in the Department of Electrical and Electronic Engineering, supervised by Prof. Can Li. She received her B.E. degree in Automation from Xi’an Jiaotong University, China in 2022. Her research focuses on in-memory computing, Ising machine, analog computing, combinatorial optimization, and energy-based neural networks.
Organiser
Prof. Can Li
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Toward 6G Edge AI: The Optimization and Application of Movable Antenna and Fluid Antenna
2025-11-27 14:30 - 15:30 | Online via Zoom
Zoom Link:https://hku.zoom.us/j/97594921448?pwd=[REDACTED]
Abstract
The recently emerged movable antenna (MA) and Fluid antenna (FA) show great potential in leveraging spatial degrees of freedom for enhancing the performance of wireless systems. In future AI-embedded 6G communication networks, MA/FA has great potential to improve the quality of service of edge AI. However, resource allocation in MA/FA-aided systems faces unique challenges due to the non-convex and coupled constraints on antenna positions.
In this talk, we will systematically reveal the challenges brought by the minimum MA/FA separation constraints at first, and propose a penalty framework for resource allocation under such new constraints in MA/FA-aided systems.
Furthermore, we will also address the challenge of edge AI inference for handling the trade-off problem of model accuracy and network latency. To guarantee the high-quality of users’ service, the latency and peak signal-to-noise ratio (PSNR) of features are considered in the objective of optimization, and we propose an efficient algorithm under the block coordinate descent framework to solve this trade-off problem.

Speaker
Mr. Yichen Jin
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Yichen Jin received the B.Eng. degree from the Faculty of Automation, Nanjing University of Science and Technology, Nanjing, China, and the MSc degree from the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, in 2020 and 2022, respectively. He is currently working toward the Ph.D. degree with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong. His research interests include wireless communication and edge AI.
Organiser
Prof. Yik-Chung Wu
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Trustworthy Tree-based Machine Learning by MoS2 Flash-based Analog CAM with Inherent Soft Boundaries
2025-11-27 15:00 - 16:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/96125660975?pwd=[REDACTED]
Abstract
The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models like Random Forest and XGBoost excel in interpretability and accuracy for tabular data, but scaling them remains computationally expensive due to poor data locality and high data dependence. Previous efforts to accelerate these models with analog content addressable memory (CAM) have struggled, due to the fact that the difficult-to-implement sharp decision boundaries are highly susceptible to device variations, which leads to poor hardware performance and vulnerability to adversarial attacks. This work presents a novel hardware-software co-design approach using MoS2 Flash-based analog CAM with inherent soft boundaries, enabling efficient inference with soft tree-based models. Our soft tree model inference experiments on MoS2 analog CAM arrays show this method achieves exceptional robustness against device variation and adversarial attacks while achieving state-of-the-art accuracy. Specifically, our fabricated analog CAM arrays achieve 96% accuracy on Wisconsin Diagnostic Breast Cancer (WDBC) database, while maintaining decision explainability. Our experimentally calibrated model validated only a 0.6% accuracy drop on the MNIST dataset under 10% device threshold variation, compared to a 45.3% drop for traditional decision trees. This work paves the way for specialized hardware that enhances AI’s trustworthiness and efficiency.
Speaker
Mr. Bo Wen
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Bo Wen received his B.Eng. degree from the School of Materials Science and Engineering at Huazhong University of Science and Technology (HUST), China in 2015, and his M.Eng. degree from the University of Chinese Academy of Sciences in 2020. 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-memory computing, analog content-addressable memory, trustworthy machine learning and software-hardware co-design.
Organiser
Prof. Can Li
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Lightweight Learning for the Coordination of Distributed Energy Resources
2025-11-28 11:00 - 12:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/8957840635?pwd=[REDACTED]
Abstract
The proliferation of distributed energy resources presents significant coordination challenges due to their scale and variability. While traditional centralized methods are hindered by high communication and computational costs, resource-constrained edge devices struggle with conventional algorithms. This paper aims to bridge this gap by developing lightweight learning approaches for edge devices, enabling scalable and efficient coordination of distributed resources. The work focuses on three key analyses: descriptive analysis (non-intrusive load monitoring), predictive analysis (load forecasting), and prescriptive analysis (energy management for market participation). Ultimately, these lightweight algorithms are implemented on established hardware testbeds, paving the way for low-cost, high-efficiency coordination of massive, distributed assets.

Speaker
Mr. Yehui LI
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Yehui Li received the B.S. degree in electronic science and technology from Harbin Institute of Technology in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering with the University of Hong Kong. His current research interests include data analytics and edge intelligence in smart grids.
Organiser
Prof. Yi Wang
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Brain-Inspired Structural Optimization: Edge Pruning and Kernel Pruning Across Analog and Digital RRAM-Based Compute-in-Memory.
2025-11-28 11:00 - 12:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/93194207095?pwd=[REDACTED]
Abstract
This seminar introduces two complementary pruning strategies implemented directly on RRAM-based compute-in-memory hardware. The first approach uses the intrinsic randomness of analog RRAM electroforming to build an over-parameterized random-weight network, where edge pruning selects an efficient sub-network without requiring precise conductance tuning. This enables robust topology optimization while minimizing programming complexity.
The second approach is realized on a fully digital reconfigurable RRAM logic architecture, where in-memory XOR/AND operations measure kernel similarity and dynamically prune redundant convolution kernels during training. Together, these two pruning mechanisms illustrate a unified hardware–algorithm co-design philosophy: pruning is not a post-processing step, but a native in-memory operation that co-optimizes connectivity, computation, and resource efficiency. This synergy highlights a scalable path toward adaptive, energy-efficient RRAM-based AI accelerators.
Speaker
Mr. Songqi Wang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Songqi Wang received his B.Sc. degree 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. Han Wang. His research interests mainly include RRAM-based compute-in-memory architectures, secure and intelligent edge-computing systems, and software–hardware co-design for differential-equation-based models.
Organiser
Prof. Han Wang
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Tackling Instability and Redundancy in Diffusion-Based Generative Models
2025-11-28 14:00 - 15:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/99109748447?pwd=[REDACTED]
Abstract
This seminar presents novel solutions to tackle instability and redundancy in modern generative models. We first address the high-variance optimization challenges in Conditional Flow Matching (CFM) by introducing the Stable Velocity framework. This includes StableVM for robust training stability and StableVS, a finetuning-free accelerator that doubles sampling speed. Second, we target spatial redundancy in super-resolution via the Quadtree Diffusion Model (QDM). QDM utilizes a quadtree-guided masking strategy to focus computation solely on information-rich regions. Together, these contributions pave the way for more stable, efficient, and scalable generative models.
Speaker
Mr. Donglin Yang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Donglin Yang is an MPhil student in the Department of Electrical and Electronic Engineering, supervised by Prof. Xiaojuan Qi. He received his B.Eng. degree from Tsinghua University. His current research focuses on deep generative models, with a particular emphasis on theoretical optimization for diffusion and flow-based models.
Organiser
Prof. Xiaojuan Qi
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Collaborative Load Forecasting via Multi-Party Data Sharing
2025-11-28 15:00 - 16:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/98873959228
Abstract
Accurate load forecasting is fundamental to the stability and efficiency of modern power grids. While collaborative approaches that leverage multi-party data sharing can significantly enhance forecasting accuracy, they also introduce complex challenges. Effective collaboration is often hindered by data heterogeneity across participants, critical data privacy concerns, and the lack of clear incentives for sharing. This seminar aims to bridge this gap by presenting a comprehensive framework for collaborative load forecasting via multi-party data sharing. The work focuses on three key areas: first, handling data heterogeneity through personalization strategies; second, enhancing data privacy with distributed learning techniques; and third, fostering collaboration through an incentive-driven model trading mechanism. Ultimately, this framework paves the way for a secure, efficient, and economically viable ecosystem for multi-party collaboration, enabling more intelligent load forecasting paradigm.
Speaker
Mr. Dalin Qin
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Dalin Qin received the B.S. degree in electrical engineering and its automation from South China University of Technology in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the University of Hong Kong. His current research interests include data analytics and data sharing in smart grids.
Organiser
Prof. Yi Wang
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Generative AI-empowered Time Series Synthesis in Smart Grids
2025-11-29 11:00 - 12:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/94936719507?pwd=[REDACTED]
Abstract
The reliable operation and strategic planning of smart grids are critically dependent on high-fidelity time series data. However, the increasing stochasticity of both energy supply and demand challenges conventional analytical methods, exacerbated by potential extreme scenarios. This research posits Generative Artificial Intelligence (AI) as a transformative approach, empowering not only the synthesis of realistic load/renewable energy time series, but also their conditional generation for predictive analysis. This seminar will go through time series generation on both the supply and demand sides, and then investigate the refinement for the generated data. Finally, a Python library, GenTS, is constructed to provide a unified framework for benchmarking generative time series models under various tasks.
Speaker
Mr. Chenxi Wang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Chenxi Wang received the B.S. degree in Electrical Engineering from South China University of Technology in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering with the University of Hong Kong. His current research interests include time series analytics and generative AI in smart grids.
Organiser
Prof. Yi Wang
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – On the Understanding of Uncertainty in Load Forecasting
2025-11-29 14:00 - 14:30 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/92603659701
Abstract
In the context of digital transformation of the energy system, how to effectively manage and quantify the uncertainty in forecasting has become a key bottleneck that restricts its reliable operation. This report will introduce our systematic work in the field of probabilistic load forecasting, dedicated to addressing this core challenge. Our research establishes a complete solution around uncertainty, covering three key aspects: firstly, data preprocessing, aimed at reducing the uncertainty of raw data; Next is model construction, which precisely quantifies the uncertainty of predictions through innovative deep learning models; Finally, the model explanation provides a unified and clear explanation framework for the probabilistic forecasting model of the “black box”. Through this series of studies, we have not only significantly improved forecasting accuracy, but also developed an open-source toolkit aimed at promoting the practical application of high reliability load forecasting technology in future energy systems.
Speaker
Mr Zhixian Wang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Zhixian Wang received the B.S. degree in Statistics from The University of Science and Technology of China in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering with the University of Hong Kong. His current research interests include application of AI techniques in power data analytics.
Organiser
Prof. Yi Wang
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Trustworthy data sharing in power systems via blockchain
2025-11-29 14:30 - 15:00 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/99852061481?pwd=[REDACTED]
Abstract
With the digitalization of smart grids, data becomes vital for advanced applications like load forecasting, energy management, and demand response. To unlock its full potential, the critical challenge becomes how to build a trustworthy data-sharing framework for diverse stakeholders. Blockchain stands out as a promising solution. In this report, we introduce a comprehensive framework to support both direct and implicit sharing methods via blockchain. For direct sharing, we introduce a blockchain based searchable encryption for secure data retrieval from the cloud. For implicit sharing, we propose a blockchain assisted federated framework to achieve collaborated training. To realistically deploy blockchain within existing infrastructure, an optimization approach for node deployment is proposed to ensure practical implementation. Through this series of framework constructions, we demonstrate the significant potential of blockchain applications in building a secure and efficient data-sharing ecosystem for the next generation of smart grids.

Speaker
Mr. Ruiyang Yao
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Ruiyang Yao received the integrated master’s degree in mathematics from University of Oxford in 2021. He received the MSc in computing from Imperial College London in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering with the University of Hong Kong. His current research interests include trustworthy data sharing in power systems.
Organiser
Prof. Yi Wang
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – High-throughput Neuromorphic Computational Imaging
2025-12-01 10:30 - 11:30 | Online via Zoom
Zoom Link: https://hku.zoom.us/j/99712347936?pwd=[REDACTED]
Abstract
High-throughput dynamic imaging must recover fine spatial structure under rapid motion, yet no conventional sensor can fully overcome the trade-offs between spatial resolution, temporal resolution, and motion-induced degradation. Frame sensors inevitably blur fast dynamics due to global integration, while event sensors, although extremely fast and high-dynamic-range, provide only local 1-bit temporal changes and lack global spatial context. These sensing limitations fundamentally constrain applications ranging from defect inspection to phase-flow analysis. In this seminar, I will present a neuromorphic computational imaging paradigm, Neuromorphic Super-Resolution (NeuroSR), that addresses these limitations through physics-informed spatio-temporal feature inference. NeuroSR unifies the complementary measurements of frames and events into a fully differentiable architecture, enabling high space–time resolved reconstruction and direct inference of physical structure such as motion blur kernels or coherent wave propagation. To illustrate the generality of this paradigm, I will also introduce Neuromorphic Wave-Normal Sensing (NeuroSH) as a representative white-box example. NeuroSH demonstrates how asynchronous event cues can recover large-gradient wavefront information and surpass classical spot-overlapping constraints in dynamic wavefront sensing systems. Together, these results highlight a unified neuromorphic approach that transforms both dynamic imaging and physical-structure inference, enabling ultrafast defect inspection, large-gradient wavefront analysis, and high-throughput computational imaging well beyond the limits of conventional sensors.
Speaker
Mr. Chutian Wang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Biography of the Speaker
Chutian Wang received the B.S. degree from the University of Science & Technology Beijing in 2020, and the M.S. degree at Imperial College London in 2021. He is currently working towards his Ph.D. degree with the Department of Electrical and Electronic Engineering, the University of Hong Kong. His research interests include computational neuromorphic imaging, wavefront sensing and digital holography.
Organiser
Prof. Edmund Y. Lam
Department of Electrical and Electronic Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Brain-inspired Random Memristors Pruning for Input-aware Dynamic SNN
2025-12-01 14:00 - 15:00 | 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
