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

Symposium on AI for Social Good: Understanding Energy and Neuro Complex Systems through AI
2026-02-06 15:00 - 18:00 (Asia/Hong_Kong) | Room 601, 6/F, MSc Student Commons, Pacific Plaza, 410 Des Voeux Road West
All EEE MSc students are welcome!

Seminar on Exploring the 6G Frontier: Prototyping Key Technologies at Yonsei
2026-02-10 16:30 - 18:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
This talk will begin with a brief introduction to Yonsei University and then present an overview of its recent research efforts in 6G and cloud-based wireless prototyping. Since 2011, the Yonsei team has pioneered a range of hardware-oriented research topics, including full-duplex radios, millimeter-wave lens MIMO, wireless VR and haptic communications, reconfigurable intelligent surfaces (RIS), magnetic MIMO, and semantic communications. Building on these foundational works, recent projects have expanded toward vRAN/ORAN architectures, 6G/Cloud convergence, and AI-empowered extremely large MIMO (AI-E-MIMO). The talk will conclude with a discussion of ongoing efforts and potential collaboration models for future 6G system development.
Speaker
Prof. Chan-Byoung CHAE
Ph.D., IEEE Fellow, NAI Fellow
Underwood Distinguished Professor & Lee Youn Jae Fellow,
Yonsei University
Member of National Academy of Engineering of Korea
Former Editor-in-Chief, IEEE Trans. MBMC
IEEE Distinguished Lecturer
Speaker’s Biography
Chan-Byoung CHAE is an Underwood Distinguished Professor and the Lee Youn Jae Endowed Chair Professor at Yonsei University, Seoul South Korea. Before joining Yonsei, he was with Bell Labs, Alcatel-Lucent, Murray Hill, NJ, USA, from 2009 to 2011, as a Member of Technical Staff, and Harvard University, Cambridge, MA, USA, from 2008 to 2009, as a Postdoctoral Fellow and Lecturer. He received his Ph.D. degree in electrical and computer engineering from The University of Texas at Austin (UT), USA in 2008. Prior to joining UT, he was a Research Engineer at the Telecommunications R&D Center, Samsung Electronics, Suwon, South Korea, from 2001 to 2005.
Prof. Chae was a recipient/co-recipient of the IEEE ComSoc Education Award in 2026, the Ministry of Science and ICT Award in 2024, the Ministry of Education Award in 2024, the KICS Haedong Scholar Award in 2023, the CES Innovation Award in 2023, the IEEE ICC Best Demo Award in 2022, the IEEE WCNC Best Demo Award in 2020, the Best Young Engineer Award from the National Academy of Engineering of Korea (NAEK) in 2019, the IEEE DySPAN Best Demo Award in 2018, the IEEE/KICS Journal of Communications and Networks Best Paper Award in 2018, the IEEE INFOCOM Best Demo Award in 2015, the IEIE/IEEE Joint Award for Young IT Engineer of the Year in 2014, the KICS Haedong Young Scholar Award in 2013, the IEEE Signal Processing Magazine Best Paper Award in 2013, the IEEE ComSoc AP Outstanding Young Researcher Award in 2012, and the IEEE VTS Dan. E. Noble Fellowship Award in 2008.
Prof. Chae has held several editorial positions, including Editor-in-Chief of the IEEE Trans. on MBMC, Senior Editor of the IEEE WCL, and Editor of the IEEE CommMag, and IEEE TWC. He was an IEEE ComSoc Distinguished Lecturer from 2020 to 2023 and is an IEEE VTS Distinguished Lecturer from 2024 to 2025. He is an elected member of the National Academy of Engineering of Korea and Fellow of the National Academy of Inventors (US).
Organiser
Prof. Kaibin HUANG
Department of Electrical and Electronic Engineering,
The University of Hong Kong
All are welcome!

Seminar on Cross-Species Functional MRI (fMRI) Investigations of Reinforcement Learning
2026-03-06 11:00 - 12:00 | Tam Wing Fan Innovation Wing Two, G/F, Run Run Shaw Building, The University of Hong Kong
Abstract
Reinforcement learning in humans depends on distributed neural circuits for value updating and behavioural adaptation. Cross-species comparisons, particularly with macaques, greatly facilitate our understanding of these mechanisms in humans by revealing conserved and evolved elements, but they crucially depend on precise anatomical alignment to identify homologous regions and interpret functional parallels or divergences across species.
In this talk, I will synthesise recent cross-species fMRI evidence on prefrontal contributions to reinforcement learning. I will first outline key methods for anatomical comparison that enable functional inferences across species despite marked differences in brain morphologies. I will then present findings from reversal learning tasks in humans and macaques, demonstrating conserved orbitofrontal cortex signals that support rapid value updating in response to changing reward contingencies. Next, I will discuss anterior cingulate cortex (ACC) activations in both species, which play a key role in enacting adaptive changes. Finally, I will highlight the anatomical uniqueness of the human frontopolar cortex (FPC), particularly its lateral subdivision, which lacks a clear homolog in macaques and shows emerging functional importance in our recent findings for handling higher-dimensional aspects of reinforcement learning.
Speaker
Prof. Bolton KH CHAU
Department of Rehabilitation Sciences,
The Hong Kong Polytechnic University
Speaker’s Biography
Prof. Bolton KH CHAU is an Associate Professor in the Department of Rehabilitation Sciences and Associate Director of the Mental Health Research Centre at The Hong Kong Polytechnic University. He received my DPhil from the University of Oxford and was APS Rising Star by the Association for Psychological Science. His research interests lie in decision neuroscience, with a particular focus on how the brain integrates information and sometimes arrives at irrational or biased choices. He adopts a multidisciplinary approach, combining computational modelling, behavioural experiments, brain imaging, and brain stimulation to investigate the mechanisms underlying decision-making in both simple and complex contexts. Recently, he has developed a keen interest in the frontopolar cortex, a region uniquely expanded in the human brain, and its role in supporting complex decision-making. This work is supported by the RGC Collaborative Research Fund.
Organiser
Dr. Alex Tze Lun LEONG
Department of Electrical and Computer Engineering,
The University of Hong Kong
Acknowledgement
Tam Wing Fan Innovation Wing Two

All are welcome!

Seminar on An ECE Framework for Instrumentation and Education: From Microscopy Design to Community Outreach
2026-03-17 10:00 - 11:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Advanced electron microscopy, characterised by atomic-scale resolution, is a cornerstone for observing material dynamics. The development of these instruments presents complex engineering challenges in electro-optics and system integration. Dr. Hsueh holding a PhD in Electrical and Computer Engineering, leverages his expertise in electromagnetic waves, waveguides, and imaging theory to drive the development of next-generation electro-optical systems. This talk outlines his multidimensional approach to academia through an ECE framework.
In research, Dr. Hsueh focuses on the design and development of ultrafast and quantum technologies employing scanning and transmission electron microscopy (SEM/TEM). His current work involves the commercialisation of pulsed hollow-cone hybrid electron microscopes, a project supported by the RAISe+ scheme and protected by patents. His research experience spans laser optical design, optical measurement systems, optical and THz waveguide design, optical force theory, and aperiodic nanostructure design. Regarding teaching and administration, Dr. Hsueh served as a Visiting Assistant Professor at the City University of Hong Kong (2023–2025), where he taught courses in electron microscopy, materials science, and engineering graphics. His ECE background further qualifies him to teach courses such as electromagnetics and other related subjects. Beyond the classroom, he has demonstrated significant leadership in institutional service, having organised international research conferences and contributed to the strategic planning of the university’s core facility. In the realm of knowledge transfer and outreach, Dr. Hsueh is committed to nurturing the next generation of engineers. He is currently developing and implementing AI education programs for primary and secondary school students. By bridging high-end instrumentation design with community engagement and administrative expertise, he aims to foster a robust and interdisciplinary academic ecosystem.
Speaker
Dr. Yu-Chun HSUEH
Research Fellow at City University of Hong Kong
Speaker’s Biography
Dr. Yu-Chun HSUEH received his B.S. degree in Electrical Engineering from National Tsing Hua University in 2007, his M.S. degree from the Graduate Institute of Photonics and Optoelectronics at National Taiwan University in 2009, and his PhD degree in Electrical and Computer Engineering from Purdue University in 2018. He was a Postdoctoral Researcher at Purdue University in 2018, and subsequently a Postdoctoral Fellow and Research Scientist at the City University of Hong Kong from 2019 to 2023. He served as a Visiting Assistant Professor in the Departments of Materials Science and Engineering and Mechanical Engineering at the City University of Hong Kong from 2023 to 2025, where he taught courses in electron microscopy, materials science, and engineering graphics. He is currently a Research Fellow at the City University of Hong Kong, working on the commercialisation of next-generation electron microscopes and community outreach through the implementation of AI education programs for primary and secondary school students. His research experience encompasses the theory, design, modelling, and measurement of photonics and optomechanics, ranging from the terahertz (THz) to the optical regime. During his master’s program, his research focused on low-loss THz waveguide design, resulting in 2 journal publications and 1 patent. He was inducted as an honorary member of the Phi Tau Phi Scholastic Honor Society at National Taiwan University in 2009 and received the Government Scholarship to Study Abroad from Taiwan in 2012. During his Ph.D. program, his research focused on the theory and modelling of field control, field statistics, and optomechanics with aperiodic nanostructures, with results published in Physical Review Letters and related journals. Building on his ECE background, his current research interests centre on the design and development of ultrafast and quantum technologies for scanning and transmission electron microscopy. He has been invited to present at international conferences and holds several patents for next-generation electron microscopes, supported by the RAISe+ project.

Seminar on Why Not Electric Vehicle
2026-03-17 14:30 - 15:30 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
This seminar will review some Electric Vehicle (EV) system concepts and designs, electric machines and drives for EVs, hybrid powertrains for hybrid EVs, EV energy sources and energy management systems, and EV-to-grid technology.
Speaker
Ir Dr. T. W. CHING
Department of Electrical and Computer Engineering
Speaker’s Biography
Ir Dr. T. W. CHING received the Bachelor and Master degrees in Electrical Engineering from The Hong Kong Polytechnic, and the Doctor of Philosophy in Electrical and Electronic Engineering from The University of Hong Kong. He served with the Hongkong Electric Company Limited, CLP Power Hong Kong Limited and the University of Macau. He has been with the Department of Electrical and Computer Engineering, The University of Hong Kong, since 2018. He is a Chartered Electrical Engineer as well as a Chartered Building Services Engineer. In professional service, he was a member of the Financial Committee of the IET Hong Kong and the Honorary Treasurer of Power and Energy Section of the IET Hong Kong. He was an organising committee member of the 14th, 15th, 16th, 17th, 18th and 19th Annual Power Symposium of the IET, and the 12th APSCOM. Internationally, he delivered more than 100 technical presentations and served as organiser and invited chairperson of a dozen of special sessions in international conferences. His courses are “Electric Vehicle Technology”, “Electrical Installations” and “Advanced Electric Vehicle Technology”. Recently, he created two master courses, namely “Advanced electrical energy & power conversion systems” and “Advanced optimisation & control strategies in modern power systems”. He also co-supervises PhD students in his areas of expertise.

Seminar on Integration of Renewable Energy for Power Restoration: Real-time Digital Simulation Approach
2026-03-19 15:15 - 16:15 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
The drive toward aggressive decarbonization goals is rapidly transforming the power grid, highlighted by an increase in renewable energy production. This expansion relies heavily on Distributed Energy Resources (DERs), yet operators face challenges due to the lack of transparency in DER operations. This opacity poses significant risks to grid stability as the growing number of DERs could exceed the capacity of the current power network. In response, the emergence of Digital Twins (DT) technology provides a potential solution by creating virtual replicas of the physical grid infrastructure, which require minimal data transmission. DT technology overcomes the obstacles of real-time data flow and enhances system transparency. To encourage the wider application of DT in the industry, it is crucial to develop and test its applications through practical experiments. For this purpose, Power Hardware-in-the-Loop (PHIL) experiments are used to compare the effectiveness of real power components with DT models. These experiments connect Grid-forming Inverter (GFMI) to a Real-time Digital Simulator (RTDS) for PHIL and DT testing, enabling detailed analysis of photovoltaic inverter behaviour.
This research presents a platform specifically built for immediate simulation suited to DT and PHIL methods. It is designed to prototype, demonstrate, and assess GFMIs under various critical scenarios for power restoration. By incorporating the Perez Model into the DT model through simulation exchange, the accuracy in comparison with the traditional PHIL model is enhanced. Thus, the entire restoration process can be thoroughly represented and analysed. All in all, this paper introduces a novel approach to integrating renewable energy resources using PHIL-based digital twins technology to enhance power restoration stability.
Speaker
Dr. Jason Man Hin CHOW
Lecturer at Vocational Training Council (VTC)
Speaker’s Biography
Dr. Jason Man Hin CHOW obtained a BEng from the University of Sheffield and an MSc and a PhD from The University of Hong Kong, all in Electrical and Electronic Engineering. He is now a Lecturer at Vocational Training Council (VTC) and has over 4 years of teaching experience in territory education. Before joining VTC, he joined an international consultancy firm to undergo a 2-year formal training programme for professional development. He was subsequently promoted to Project Engineer in charge of several large-scale electrical installation projects. Appointed as Deputy Manager of CLP Power Engineering Laboratory under VTC jurisdiction, he leads a team of lecturers and laboratory technicians to do experiments/projects and research in collaboration with other universities. He is a Chartered Engineer, Beam Pro, Member of IET, Member of InstMC, Member of HKIE, Member of CIBSE and Member of Building Services Operation, Maintenance and Executives Society. Dr. Chow is actively participating in local professional institutions, and he has published several conference/journal papers at international organisations/institutions. His research areas include power system control, integration of renewable energy and smart grid.

RPG Seminar – Synthetic Aperture for High Spatial Resolution Acoustoelectric Imaging
2026-03-20 09:00 - 10:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Zoom Link:
https://hku.zoom.us/j/97208194193
Abstract
Acoustoelectric imaging (AEI) refers to the mapping of electric fields in electrolyte and tissue media by measuring the acoustoelectric (AE) effect. It shows promise for non-invasive electrophysiological mapping down to the resolution of diagnostic ultrasound imaging. AE signals are typically induced by applying focused ultrasound (FUS) waves, which sift out the electric signals at a defined focal spot. However, the spatial resolution of FUS-AEI is limited by the finite focal extent. To achieve improved AEI spatial resolution across the full imaging depth, we propose to perform AEI by adopting a Synthetic Aperture (SA) approach. SA-AEI images were reconstructed through pixel-oriented delay-and-sum of the unfocused AE signals. Experiments were done on an NaCl volume and an ex vivo lobster nerve. Overall, SA-AEI exhibited superior lateral resolution compared to FUS-AEI, particularly for electric targets outside the focal zone of FUS-AEI. Due to inherently lower SNR of the SA approach, we further proposed coherence-based beamforming to enhance the image quality of SA-AEI images. We envision that proposed SA-AEI would be a useful strategy for AEI, when spatial resolution is the top imaging performance criterion and prior locations of bioelectric sources are unknown.
Speaker
Mr. Wei Yi Oon
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Wei Yi OON received his BEng in Medical Engineering from The University of Hong Kong in 2021. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Hong Kong, with a research focus on acoustoelectric imaging.
Organiser
Prof. Wei-Ning Lee
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – LLMs for Social Good: Addressing Data Scarcity and Opacity for Alzheimer’s Diagnosis and Prognosis
2026-03-24 14:00 - 15:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Zoom Link:
https://hku.zoom.us/j/94842355191?pwd=[REDACTED]
Abstract
Early detection of Alzheimer’s Disease (AD) through non-invasive speech analysis offers a highly promising diagnostic avenue. However, the development of robust computational models is severely hindered by the fundamental imperfections of real-world clinical data. Spontaneous patient speech is often noisy and highly variable, while longitudinal clinical records suffer from severe data scarcity, temporal sparsity, and missing values. Consequently, traditional deep learning models act as opaque “black boxes,” and this inherent opacity undermines the clinical trust required for real-world deployment. Furthermore, while Large Language Models (LLMs) show revolutionary potential, they too struggle to robustly model individualized disease progression from sparse data without specialized architectural integration. This leads to the central research question: How can an LLM-driven framework be systematically designed to extract clinically meaningful features and synthesize high-fidelity multi-modal data, thereby overcoming the intertwined limitations of data incompleteness and black-box opacity?
To address this, this seminar proposes an LLM-driven spatio-temporal multi-modal framework. The overarching objective is to develop theoretically grounded methodologies that leverage LLMs to robustly distill raw patient speech into structured Cognitive-Linguistic (CL) atoms and interpretable linguistic markers. Concurrently, the framework integrates qualitative medical knowledge and synthesizes rich, realistic training samples to effectively enrich decision boundaries in data-deficient environments. This research significantly advances AI for Social Good by providing a scalable, low-cost methodology for early dementia screening that reduces the reliance on invasive and expensive traditional diagnostics.
Speaker
Mr. Tingyu MO
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Mr. Tingyu MO is a Ph.D. candidate with the Advanced Well-being and Society Research Platform (AI-WiSe) at The University of Hong Kong, under the supervision of Prof. Victor O.K. Li, Prof. Jacqueline C.K. Lam, and Prof. Yunhe Hou. He received his B.S. degree in Intelligence Science and Technology from the University of Science and Technology Beijing in 2021, and his M.Eng. degree in Electronic and Information Engineering from Beihang University. His research interests include AI for Social Good, with a specific focus on Alzheimer’s diagnosis and prognosis.
Organiser
Prof. Victor O.K Li, Prof. Jacqueline C.K Lam, Prof. Yunhe Hou
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Scaling Up Spatial Awareness: High-Fidelity Data Synthesis for 3D Scene Understanding
2026-03-26 17:00 - 18:00
Zoom Link:
https://hku.zoom.us/j/91627715757?pwd=[REDACTED]
Abstract
Spatial understanding constitutes a fundamental pillar of human-level intelligence, yet its advancement is currently bottlenecked by the scarcity of diverse, high-fidelity 3D data. Existing research predominantly relies on domain-specific or manually annotated datasets, creating a critical void: the absence of a principled, scalable engine capable of synthesizing high-quality spatial data at scale. To address this, we elucidate the core design principles for robust spatial data generation and introduce OpenSpatial—an open-source engine engineered for high fidelity, massive scalability, and broad task diversity. OpenSpatial adopts 3D bounding boxes as the foundational primitive to architect a comprehensive data hierarchy across five essential dimensions: Spatial Measurement, Spatial Relationship, Camera Perception, Multi-view Consistency, and Scene-Aware Reasoning. Leveraging this infrastructure, we curate OpenSpatial-3M, a large-scale dataset that enables models to transition from simple recognition to sophisticated spatial intelligence. Extensive evaluations demonstrate that models trained on our synthesized data achieve state-of-the-art performance across a wide spectrum of benchmarks, showing substantial and consistent improvements over existing baselines. Furthermore, we provide a systematic analysis of how synthesized data attributes influence the emergence of spatial perception in vision-language models. By open-sourcing both the engine and the 3M-scale dataset, we offer a versatile foundation to accelerate future research in generalized 3D scene understanding.
Speaker
Mr. Jianhui Liu
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Mr. Jianhui Liu is a PhD candidate with the Department of Electrical and Electronic Engineering at the University of Hong Kong. He received the B.Eng. degree in Intelligent Science and Technology from Xidian University in 2021. His research interest lies in machine learning and computer vision, focusing on Multimodal Large Language Models (MLLMs) for reasoning, agent, long video, spatial intelligence, unified models, and their real-world grounding and applications.
Organiser
Prof. Xiaojuan Qi
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Broadband Mamyshev Oscillator at 1.7 μm for Multicolor Three-photon Fluorescence Microscopy
2026-03-31 14:00 - 15:00
Zoom Link:
https://hku.zoom.us/j/95690107722?pwd=[REDACTED]
Abstract
Three-photon fluorescence (3PF) microscopy enables high-contrast deep-tissue imaging with cellular resolution, especially in 1.7 μm wavelength range, yet its widespread adoption has been hindered by the lack of compact, tunable, and high-power femtosecond laser sources. Here, we demonstrate a broadband tunable ultrafast Mamyshev oscillator operating in the 1.7 μm wavelength region, specifically designed for multicolor 3PF microscopy. The all-fiber ring cavity, incorporating two arms with tunable grating-based filters, generates stable ultrashort pulses with flexibly tunable central wavelength from 1730 nm to 1810 nm and adjustable bandwidth up to 140 nm at 10 dB. The oscillator at 7.14-MHz repetition rate are amplified using a chirped pulse amplification (CPA) system to achieve 80-nJ pulses with a slope efficiency of 46.2%, and finally compressed to 65 fs. We showcase the versatility of this laser source through various imaging modalities. High-contrast, label-free third-harmonic generation (THG) images of diverse biological samples are presented. Deep-tissue vasculature 3PF images in an ex vivo mouse brain down to a depth of 1 mm are visualized. Crucially, we achieve multicolor 3PF imaging with a single excitation wavelength for various co-labeled mouse brain samples, visualizing the interaction between neurons and plaques with distinct morphologies in an Alzheimer’s disease mouse model. This compact, tunable, and high-power 1.7 μm ultrafast fiber laser establishes a powerful tool for advanced biomedical imaging, particularly for deep tissue and multiplexed studies of neurodegenerative diseases.
Speaker
Miss Xiaoxiao Wen
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Xiaoxiao Wen received her bachelor’s degree and the master’s degree from the South China University of Technology (SCUT) in 2019 and 2022, specializing in ultrafast laser dynamics measurement. She is currently a PhD candidate at the Department of Electrical and Electronic Engineering, the University of Hong Kong, under the supervision of Prof. Kenneth Kin-Yip Wong. Her current research interests include ultrafast fiber laser, fiber nonlinearities, ultrafast measurement, multiphoton microscopy, and optical neural networks.
Organiser
Prof. Kenneth Kin-Yip Wong
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

Seminar on Exploring the Feasible Net-Zero Transition Pathway in China
2026-04-01 10:30 - 11:30 | Room CB-601J, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Net-zero energy transition is projected to accelerate the replacement of fossil fuels with renewables, leaving system flexibility resources increasingly scarce. Here, we present a sub-annual energy-environment-economy model with endogenous hourly energy demand profiles and power balance dynamics, including power dispatch, storage operations and demand-side response that co-optimises supply- and demand-side flexibility, to map feasible transition pathways for China. The results show that compared with coarser timeslice representative of common modelling practice, sub-annual representation tightening flexibility needs with a high variable renewable energy and high electrification energy system. Accounting for temporal variability in supply and demand, the cost-optimal solution exhibits marginal abatement costs that are over 9% higher, but incorporating demand-side flexibility measures can mitigate cost growth and delineate least-regret portfolios for reliable, affordable decarbonization. Incentives for demand-side response, such as load time-shifting and vehicle-to-grid can reduce investment in pumped hydro by 23% and yield more than a threefold cost-benefit ratio. The study highlights enhanced modelling of temporal dynamics within future energy model development and incentive-compatible market mechanism design for dispatchable resource development.
Speaker
Prof. Shu ZHANG
Assistant Professor,
Institute of Energy, Environment and Economy,
Tsinghua University
Speaker’s Biography
Prof. Shu ZHANG is an Assistant Professor at the Institute of Energy, Environment and Economy, Tsinghua University. He holds a B.S. in Electrical Engineering and a PhD in Management from Tsinghua, and was a visiting scholar at IIASA (2022-2023). He currently leads multiple national and international projects, including those funded by the NSFC and National Key R&D Programs. His research focuses on cross-sector synergies across energy, land, water, and air quality systems. By establishing and applying a domestically developed energy-environment-economy model, he explores integrated low-carbon pathways across the energy, transport, buildings, and industry sectors. With publications in Nature Sustainability and Nature Communications, Prof. Zhang provides evidence-based insights to support strategic policymaking in low-carbon transitions.
Organiser
Prof. Yi WANG
Department of Electrical and Computer Engineering,
The University of Hong Kong

All are welcome!

Seminar on Can Industrial Overcapacity Enable Seasonal Flexibility in Electricity Use?
2026-04-01 14:00 - 15:00 | Room CB-601J, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
In many countries, declining demand in energy-intensive industries such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock energy-intensive industries’ flexibility in electricity use. Here, using China’s aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining energy-intensive industries overcapacity for flexible electricity use in decarbonised energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China’s decarbonised electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry’s product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also provide an opportunity for seasonally complementary labour deployment across the aluminum smelting and thermal power generation sectors, offering a potential pathway for mitigating socio-economic disruptions caused by industrial restructuring and energy decarbonization.
Speaker
Mr. Ruike LYU
5th year PhD. Candidate,
Department of Electrical Engineering,
Tsinghua University
Speaker’s Biography
Mr. Ruike LYU received his bachelor’s degree in Electrical Engineering from Tsinghua University, Beijing, China, in 2021, and is currently pursuing a PhD degree there. He was a one-year visiting scholar at Princeton University. His research interests include demand-side flexibility from electric vehicles, commercial buildings, and industrial loads. He has been recognised for his work with several awards, including Best Paper at multiple conferences such as CEEPE 2024, PESGM 2025, and EECT 2025. He also received the Best Presentation Award at the IEEE PES PhD. Dissertation Challenge in 2025.
Organiser
Prof. Yi WANG
Department of Electrical and Computer Engineering,
The University of Hong Kong

All are welcome!

RPG Seminar – Efficient AI for Neural Signal Decoding in Healthcare Applications
2026-04-10 10:00 - 11:00
Zoom Link:
https://hku.zoom.us/j/93628966280
Abstract
Recent advances in artificial intelligence have brought new momentum to healthcare driven by neural signal decoding. This seminar presents three lines of our research on AI-based neural signal analysis across diverse application domains, including wearable EEG-based epileptic seizure detection, emotion recognition in SSVEP-based brain–computer interfaces (BCIs), and multimodal neuroimaging-based tinnitus diagnosis. First, for real-time epileptic seizure detection on resource-constrained wearable devices, we propose a multi-scale LBP-based hyperdimensional computing framework that captures seizure-related temporal dynamics with a compact model size, strong few-shot learning capability, and improved interpretability. Second, to enhance emotion-aware interaction in SSVEP-based BCIs, we develop a valence-arousal disentangled representation learning method that separates core emotional factors, extracts global affective features, and improves cross-subject generalization. Third, for objective tinnitus diagnosis, we introduce TinnitusLLM, a multimodal large language model that integrates EEG and fMRI through neuro-inspired positional encoding, multimodal autoregressive pretraining, and subject-invariant cross-modal fine-tuning. Across these studies, our common goal is to build efficient, interpretable, and clinically meaningful learning frameworks for robust neural decoding in real-world healthcare and human–machine interaction scenarios.
Speaker
Mr Yipeng Du
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Yipeng Du is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at The University of Hong Kong. He received the B.E. degree in Communication Engineering from the University of Science and Technology Beijing and the M.E. degree in Signal and Information Processing from Peking University. His research focuses on developing deep learning methods for neural signal processing in healthcare, particularly for disease diagnosis, monitoring, and brain–computer interface applications.
Organiser
Prof Edith C.H. Ngai
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

Seminar on Mechanism Design for the Evolving Landscape of Sustainable Power Systems
2026-04-13 11:00 - 12:30 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Modern power systems are undergoing rapid transformation driven by the integration of emerging technologies, diverse participants, and innovative business models. These changes offer unprecedented opportunities to enhance system reliability within existing infrastructure, reduce electricity costs, and accelerate renewable energy adoption. However, they also introduce complex market interactions that require rigorous analytical frameworks to ensure efficiency, fairness, and sustainability.
In this talk, the speaker will discuss our recent research on leveraging game-theoretic modelling to analyse and design mechanisms for these evolving power systems. His studies examine the impacts of coincident peak-shaving programs and energy storage participation in wholesale electricity markets through competitive equilibrium models. He also explores optimal pricing strategies for voluntary renewable energy contracts that promote large-scale renewable deployment. Together, these results underscore the importance of a principled understanding of emerging market structures and provide guidance for designing future policies that balance economic efficiency, reliability, and sustainability in power systems.
Speaker
Prof. Bolun XU
Assistant Professor,
Columbia University
Speaker’s Biography
Prof. Bolun XU is an Assistant Professor in Earth and Environmental Engineering at Columbia University, with an affiliated appointment in Electrical Engineering. He received his PhD from the University of Washington and his M.S. from ETH Zurich, both in Electrical Engineering. He also received dual bachelor degrees from Shanghai Jiaotong University and University of Michigan Ann Arbor in Electrical and Computer Engineering. Before joining Columbia, he was a Postdoctoral Fellow at the MIT Energy Initiative. His research focuses on the design and optimisation of sustainable energy and power systems and the integration of emerging technologies. He is a recipient of the NSF CAREER Award, the Outstanding Young Investigator Award from the IISE Energy Systems Division, and the Early Career Award from the INFORMS Energy, Natural Resources, and Environment (ENRE) Section.
Organiser
Prof. Yi WANG
Department of Electrical and Computer Engineering,
The University of Hong Kong

All are welcome!

RPG Seminar – Doppler LiDAR Motion Planning for Highly-Dynamic Environments
2026-04-15 15:30 - 16:30
Zoom Link:
https://us05web.zoom.us/j/83514440427?pwd=[REDACTED]
Abstract
Existing motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To address this limitation, we propose integrating motion planners with Doppler LiDARs which provide not only ranging measurements but also instantaneous point velocities. However, this integration is nontrivial due to the dual requirements of high accuracy and high frequency. To this end, we introduce Doppler Planning Network (DPNet), which tracks and reacts to rapid obstacles using Doppler model-based learning. Particularly, we first propose a Doppler Kalman neural network (D-KalmanNet) to track the future states of obstacles under partially observable Gaussian state space model. We then leverage the estimated motions to construct a Doppler-tuned model predictive control (DT-MPC) framework for ego-motion planning, enabling runtime auto-tuning of the controller parameters. These two model-based learners allow DPNet to maintain lightweight while learning fast environmental changes using minimum data, and achieve both high frequency and high accuracy in tracking and planning. Experiments on both high-fidelity simulator and real-world datasets demonstrate the superiority of DPNet over extensive benchmark schemes.
Speaker
Mr Wei Zuo
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Wei Zuo received his bachelor’s degree from Beijing Institute of Technology (BIT) in 2024, majored in Automation. He is currently an M.Phil. candidate at the Department of Electrical and Electronic Engineering, the University of Hong Kong, under the supervision of Prof. Yik-Chung Wu. His current research interests include robot perception and motion planning .
Organiser
Prof. Yik-Chung Wu
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Evolving LEO Satellite Constellations into Multi-Service Platforms
2026-04-16 10:00 - 11:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Low Earth orbit (LEO) satellite constellations offer global communication capabilities, yet fundamental system challenges restrict their full utility. Specifically, under increasing user demands, these networks face difficulties in optimizing internal traffic distribution and efficiently delivering advanced content services. Furthermore, seamlessly integrating space-based infrastructure with terrestrial systems to ensure resilient operations remains an unresolved issue. Solving these challenges is required to transition LEO constellations from basic connectivity relays into integrated service platforms.
This presentation explores optimization and extension strategies for evolving LEO satellite networks. Focusing first on the internal constellation network, we begin with Matchmaker to address traffic distribution challenges through effective load balancing to enhance network performance. To ensure quality of service (QoS) in large-scale constellations, an efficient heuristic algorithm that minimizes maximum satellite utilization and handovers is proposed. With this optimized network foundation, we elevate the constellation’s capabilities through SpaceCache+, transforming it into a space-based content delivery network (CDN). SpaceCache+ deploys cache-equipped satellites and optimizes cache replacement by managing dynamic coverage and regional preferences through zero-shot meta-learning and spatial-temporal mixing. Having expanded the constellation’s internal capacity, we finally project its utility outward to assist terrestrial infrastructure via SpaceHelper. This work integrates satellite resources with terrestrial software-defined wide area networks (SD-WANs) to construct a resilient out-of-band control plane, ensuring rapid reconnection during terrestrial link failures. Together, these works illustrate a continuous system evolution, advancing LEO networks from basic Internet access to comprehensive space-based multi-service platforms.
Speaker
Mr Dou Songshi
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Songshi Dou received B.E. degree from North China Electric Power University, Beijing, China, in 2019, and M.E. degree from Beijing Institute of Technology, Beijing, China, in 2022. He is currently pursuing Ph.D. degree at The University of Hong Kong, Hong Kong SAR, China. His research interests cover satellite Internet, software-defined networking, and traffic engineering.
Organiser
Prof. Lawrence K. Yeung
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

Seminar on Machine Learning for Integrated Sensing and Communication
2026-04-16 11:00 - 12:00 | Room CB-601J, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Integrated sensing and communication (ISAC) is a key technology for the sixth-generation (6G) wireless networks, where the same spectral and hardware resources are used for both communication and environmental sensing. Many optimization problems in ISAC require accurate sensing and communication channel models, which are often difficult to obtain. Machine learning (ML) is a powerful tool for solving ISAC problems by enabling data-driven solutions that can bypass the reliance on explicit models. This talk will explore how ML techniques can improve ISAC performance beyond traditional optimisation approaches. Two case studies will be discussed: sensing-assisted predictive beamforming and cooperative sensing through ML. These examples will demonstrate the potential of ML to enable end-to-end signal processing for ISAC in 6G wireless networks.
Speaker
Prof. Vincent WONG
Professor,
Department of Electrical and Computer Engineering,
University of British Columbia, Vancouver, Canada
Speaker’s Biography
Vincent WONG is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Vancouver, Canada. His research areas include protocol design, optimisation, and resource management of communication networks, with applications to the Internet, wireless networks, smart grid, mobile edge computing, and Internet of Things. Dr. Wong is the Editor-in-Chief of the IEEE Transactions on Wireless Communications. He is a Fellow of the IEEE, Canadian Academy of Engineering, and the Engineering Institute of Canada.
Organisers
Prof. Kaibin HUANG & Prof. Xianhao CHEN
Department of Electrical and Computer Engineering,
The University of Hong Kong

All are welcome!

RPG Seminar – System Design for Wide-field Miniature Fluorescence Microscope
2026-04-17 09:00 - 10:00
Zoom Link:
https://us05web.zoom.us/j/89432980495?pwd=[REDACTED]
Abstract
Miniature fluorescence microscope, or miniscope, is a compact fluorescence imaging system that integrates the excitation source, optical filters, imaging optics, and image sensor into a small platform. Among various technical approaches, one-photon widefield miniscopes are especially attractive because of their relatively simple architecture, strong engineering feasibility, and compatibility with two-dimensional superficial imaging tasks. However, existing miniscope systems still face important challenges, particularly in balancing a large field of view with high spatial resolution, maintaining image quality across the full field, and achieving compact system integration.
This seminar focuses on the optical design of one-photon widefield miniature fluorescence microscopes for two-dimensional superficial imaging, with the main emphasis placed on a single-channel system. The study investigates the key design considerations of excitation and emission light separation, imaging optics configuration, aberration control, and system compactness, aiming to achieve high-resolution fluorescence imaging over a relatively large field of view within a miniature platform. On this basis, scalable multi-channel architectures are briefly considered as extensions to examine the feasibility of multi-channel integration in miniature fluorescence imaging systems. The work provides a system-oriented design framework for one-photon widefield miniscopes and highlights practical strategies for improving imaging performance, structural integration, and architectural scalability.
Speaker
Mr Wentao Ye
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Wentao Ye is an MPhil student in the Department of Electrical and Computer Engineering at The University of Hong Kong, supervised by Prof. Feifei Wang. He received his B.S. degree in Applied physics from Tianjin University in 2024. Her research interests include miniature fluorescence microscope and biomedical image processing.
Organiser
Prof. Feifei Wang
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints
2026-04-17 14:30 - 15:30
Zoom Link:
https://hku.zoom.us/j/99219579381?pwd=[REDACTED]
Abstract
Deep learning (DL)-based methods have achieved remarkable success in continuous wireless resource allocation, yet its application to problems involving discrete variables remains hindered by the zero-gradient issue, complex constraint enforcement, and the lack of non-same-parameter-same-decision (non-SPSD) guarantees. To address these challenges, this seminar presents a novel DL framework that leverages probabilistic modeling of the discrete support set to enable efficient, end-to-end optimization. The speaker will further demonstrate the efficacy of this framework through two challenging mixed-discrete wireless resource allocation applications: (a) joint user association and beamforming in cell-free systems, and (b) joint antenna positioning and beamforming in movable antenna-aided systems. The results demonstrate the superior performance of the proposed framework compared to the state-of-the-art optimization algorithms and learning-based methods.
Speaker
Mr. Yikun WANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Yikun Wang received the B.E. degree in Communication Engineering from Beihang University. He is currently pursuing the M.Phil. degree in the Department of Electrical and Computer Engineering at The University of Hong Kong, under the supervision of Prof. Yik-Chung Wu. His research interests include deep learning-based optimization for wireless systems.
Organiser
Prof. Yik-Chung WU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Fast customization of color centers in single nanodiamonds and its applications
2026-04-21 11:00 - 12:00
Zoom Link:
https://hku.zoom.us/j/91528535994?pwd=[REDACTED]
Abstract
Deterministic control of nitrogen–vacancy (NV) centers in nanodiamonds (NDs) is critical for emerging applications in quantum photonics and high-density optical data storage. Conventional approaches based on random irradiation and thermal annealing often lead to nondeterministic NV formation and lattice damage, making it difficult to precisely control the optical properties of the color centers and resulting in low fabrication yields. In this seminar, we present a rapid and targeted approach for customizing NV centers in pre-selected single nanodiamonds using a standard transmission electron microscope (TEM). A brief 1-second electron beam exposure enables NV formation without post-annealing while minimizing lattice damage. The resulting nanodiamonds exhibit stable optical and spin properties. Furthermore, controlled TEM exposure can quench NV photoluminescence within tens of seconds, enabling a fast write–erase process. Leveraging this capability, we demonstrate a flexible nanodiamond-based optical data storage platform. This accessible and highly targeted method provides a powerful route for on-demand NV center engineering and opens new opportunities for quantum technologies and nanoscale photonic data storage.
Speaker
Miss Jiaqi Li
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Jiaqi Li received her BEng degree in Physics from South China Normal University in 2022. She is currently pursuing a PhD in the Department of Electrical and Computer Engineering at the University of Hong Kong. Her research focuses on the fabrication, characterization, and applications of nitrogen-vacancy (NV) centers in diamond materials, particularly in nanodiamonds.
Organiser
Prof. Zhiqin Chu
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – From 2D to 3D: Enhancing Ultrasound Resolution through Null Subtraction Beamforming
2026-04-24 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/99658715385?pwd=[REDACTED]
Abstract
High-frame-rate ultrasound imaging is a cornerstone of modern medical diagnostics and structural monitoring, yet it remains bound by the classical trade-off between spatial resolution and acquisition speed. While conventional delay-and-sum (DAS) beamforming is the industry standard, its resolution is fundamentally limited by the transducer’s diffraction pattern. This seminar introduces Null Subtraction Imaging (NSI), a nonlinear beamforming strategy designed to surpass these diffraction limits. We begin by establishing the principles of 2D NSI, where thee apodization windows are used to synthesize a “null” beampattern that effectively sharpens the mainlobe. Building on this foundation, the talk focuses on the extension of this technique into 3D NSI. We discuss the unique challenges of volumetric imaging, including the complexity of 2D aperture design and the computational demands of matrix arrays. By leveraging specialized apodization masks and hardware-efficient sampling strategies, 3D NSI achieves improvements in both azimuthal and elevational resolution, as well as contrast ratio, over DAS. The presentation will detail the theoretical framework, simulation results, and experimental validation, demonstrating a scalable path toward high-resolution, real-time 4D ultrasound imaging.
Speaker
Mr Bingze Dai
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Bingze Dai received his M.S. in Electrical and Computer Engineering at University of Illinois, Urbana-Champaign in 2023, and B.S. in Electronic and Information Engineering at Beijing Institute of Technology in 2019. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Hong Kong, with a research focus on ultrasound imaging.
Organiser
Prof. Wei-Ning Lee
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Multi-Dimensional Nano-Printing of Colloidal Quantum Dots for Infrared Optoelectronics
2026-04-24 14:00 - 15:00
Zoom Link: https://hku.zoom.us/j/92581464035?pwd=[REDACTED]
Abstract
Additive manufacturing enables the bottom-up fabrication of tailored and functional optoelectronic devices. However, realizing printed (opto)electronics with micro- and nanoscale features and multi-dimensional structures remains challenging.
Here, we developed electrohydrodynamic (EHD) printing as the platform for the in-situ deposition of sub-micron-resolution colloidal quantum dot (QD) inks from quasi-2D monolayers to 2D lines and films, and to quasi-3D walls.
By optimizing printing parameters, such as the amplitude and offset of the applied waveform, as well as the printing speed, uniform and continuous 2D QD lines with linewidths as small as 300 nm can be obtained without post-processing.
Then we performed solvent engineering on the QD ink, enabling printed QDs to form closely packed quasi-2D monolayers and even short-range-ordered hexagonal superlattices. Besides, by stacking multiple printed 2D lines, our printer enables the fabrication of quasi-3D QD walls with aspect ratios ranging from 0.25 to 2.
Finally, we demonstrate QD patterning with a resolution exceeding 10,000 dots per inch (dpi) and tunable thickness. The multi-dimensional EHD-printed QD arrays can serve as color-conversion layers for micro-LEDs (thicknesses over 1 μm) and also hold promise as a new solution for the patterning of QD-LED emissive layers (thicknesses less than 100 nm).
Speaker
Mr. Zeqi WANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Zeqi Wang received his BEng in Optoelectronic Information Science and Engineering from Southern University of Science and Technology in 2024. He is currently pursuing an MPhil degree with Prof. Zhao in the Department of Electrical and Computer Engineering, with a research focus on electrohydrodynamic printing of infrared colloidal quantum dots.
Organiser
Prof. Leo Tianshuo ZHAO
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – A BO-based Method for Parameter Tuning in Voltage Controlled DERs to Improve Flexibility Area at TSO-DSO Interface
2026-04-24 15:00 - 16:00
Zoom Link:
https://us05web.zoom.us/j/82211644170?pwd=[REDACTED]
Abstract
The rapid proliferation of aggregated flexible resources in active distribution systems (ADSs) calls for accurate estimation of the flexibility area (FA) at the transmission system operator–distribution system operator (TSO–DSO) interface to improve operational coordination. Meanwhile, voltage control plays a critical role in maintaining both economic and secure grid operation. However, the relationship between voltage control parameters and the FA metric is highly nonlinear and analytically intractable, as inverter droop coefficients and other control parameters interact with network constraints and distributed energy resource (DER) operating conditions in a coupled manner.
To address this challenge, this paper proposes a Bayesian optimization (BO)-based framework that systematically tunes voltage support parameters to maximize the FA potential. The main contributions are threefold: (1) a cross-layer optimization paradigm that links inverter-level droop control with systemlevel FA characteristics, revealing how voltage control curves shape interface power flow dynamics; (2) a reformulated FA estimation process in which the Interval-Constrained Power Flow (ICPF) model, embedded with droop-control operations, is cast as a Bayesian black-box problem solved efficiently via Gaussian process surrogates with a tile-coding kernel; and (3) a composite performance index, namely the Secure Area–Cost Flexibility (SeCoF), which jointly quantifies the trade-off between flexibility enhancement and operational cost. Case studies on modified IEEE 13-bus and IEEE 37-bus distribution systems demonstrate the effectiveness, robustness, and scalability of the proposed method in improving both technical flexibility and economic performance under realistic operational constraints.
Speaker
Miss Zihui HONG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Zihui Hong received his BEng in Electrical and Electronic Engineering from Huazhong University of Science and Technology in 2024. She is currently pursuing an MPhil degree with Prof. Hou in the Department of Electrical and Computer Engineering, the University of Hong Kong with a research focus on Bayesian Methods for Estimation and Optimization of Flexibility Region in Power Systems.
Organiser
Prof. Yunhe HOU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

HKUECE: RoboLeague 2026 cum RoboCup Junior China Open (Hong Kong)
2026-04-25 09:00 - 17:30
*** Application deadline is on April 2, 2026 (Thursday) at 8:00 pm ***

🚀 Join the Excitement of Robotics!
🌟 Please stay tuned to event-day announcements for any schedule adjustments or important updates! 🤩
Competition Details
📅 Date: April 25, 2026 (Saturday)
🕒 Time: 9:00 am – 5:30 pm
📍Venue: The University of Hong Kong
Competition Description
Engage your autonomous robots in thrilling challenges:
– Soccer League (Open) （足球公開租）
– Soccer League (Lightweight) （足球輕量組）
– Soccer League (Standard Platform) （足球標準平台組）
– Rescue League (Line) （搜救循線組）
– Rescue League (Maze) （搜救迷宮組）
– Mini-rescue League （迷你救援組）
Who Can Join?
Target Audience: Secondary School Students Team
Formation: 2-4 students ( any Forms ) per team from the same school
Key Dates
*Briefing Session:
📅 Date: March 28, 2026 (Saturday) @1:00 pm
📍Venue: Online via Zoom
*Application Deadline:
📅 Date: April 2, 2026 (Thursday) @8:00 pm
Registration & More Details
https://linktr.ee/hkuecerl
Enquiries
Please contact us via WhatsApp at 5793 9462 or email at hkuecerl@gmail.com, using the subject line “HKUECERL2026 Enquiry”.

RPG Seminar – Toward Trustworthy Network Intrusion Detection: From Representation Learning to Multi-Agent Orchestration
2026-04-27 10:30 - 11:30
Zoom Link:
https://hku.zoom.us/j/94349548162?pwd=[REDACTED]
Abstract
Modern network intrusion detection systems face critical challenges in open and dynamic environments, including concept drift as threat patterns evolve, label noise as annotations are inherently unreliable, and limited interpretability as detection decisions are difficult to understand and trust. This seminar presents our recent progress toward a trustworthy intrusion detection paradigm. We propose a representation enhancement framework that learns drift-aware and noise-robust feature embeddings, enabling detectors to sustain high accuracy under non-stationary and imperfectly labeled conditions. We further introduce an LLM-based hierarchical multi-agent system that brings semantic reasoning into intrusion detection, delivering both strong detection performance and human-interpretable explanations. These works chart a path from robust representation learning to next-generation agentic inference, advancing network intrusion detection toward greater robustness, adaptivity, and trustworthiness.
Speaker
Mr Shuo YANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Shuo Yang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at The University of Hong Kong, under the supervision of Prof. Edith C. H. Ngai. His current research interests include Network Security, Trustworthy AI, Data Mining and LLM Agent.
Organiser
Prof Edith C. H. NGAI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Mobile Reasoning-as-a-Service via Distributed LLM Inference-Time Scaling
2026-04-27 14:30 - 15:30
Zoom Link:
https://hku.zoom.us/j/6589092185?pwd=[REDACTED]
Abstract
Inference-time scaling has emerged as an effective approach for enhancing the capabilities of Large Language Models (LLMs), addressing the growing demand for stronger reasoning without increasing model size. This novel form of LLM scaling comprises two representative approaches: explicit reasoning, which generates intermediate chain-of-thought tokens during an explicit thinking phase, and implicit reasoning, which iteratively updates hidden states in the latent space without producing explicit outputs. Despite their effectiveness, both paradigms incur substantial computational and memory overhead, raising challenges for deployment on resource-constrained edge devices. To address these issues, we propose a Mobile Reasoning-as-a-Service framework that treats reasoning as a computational service accessible to edge devices over wireless networks. Focusing on implicit reasoning, we leverage its recursive structure to partition hidden-state updates between edge devices and servers, enabling cooperative inference that allows devices to access additional cloud computation on demand. To handle dynamic wireless conditions and optimize long-term performance, we formulate a joint computation and communication scheduling problem and solve it using a semantic Mixture-of-Experts (MoE)-based Soft Actor-Critic (SAC) algorithm to address heterogeneity in wireless conditions and task demands. Ultimately, this work validates distributed inference-time scaling through semantic-aware collaborative reasoning services, offering a scalable and efficient paradigm for deploying advanced LLM reasoning at the mobile edge
Speaker
Mr. Guanchen LIU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Guanchen Liu received his B.S. degree from the Harbin Institute of Technology. He is currently an MPhil candidate in the Department of Electrical and Computer Engineering at the University of Hong Kong, with a research focus on LLM reasoning.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Intrinsic Wavelength Tuning in InGaN/GaN MQWs Nano/MicroLEDs
2026-04-29 16:00 - 17:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Intrinsic wavelength tuning in InGaN/GaN multiple quantum wells (MQWs) is a critical aspect of optimizing their performance for various applications, including display technologies and visible light communication. This project investigates the underlying mechanisms of wavelength tuning in InGaN/GaN MQWs and explores strategies to achieve precise control over the emission wavelength. Through a combination of experimental studies and theoretical modeling, we analyze the effects of piezoelectric strain, indium composition, and external electric field on the emission properties of nano/microLEDs. The findings provide insights into the design and fabrication of LEDs with tuable emission characteristics, contributing to advancements in optoelectronic device engineering.
Speaker
Mr. Bo LU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Bo LU received his BSc degree in Physics from the China University of Mining and Technology in 2018 and MSc from the Southern University of Science and Technology (HIT joint program). He is currently pursuing a PhD in the Department of Electrical and Computer Engineering at the University of Hong Kong. His research focuses on the fabrication, characterization, and applications of long-wavelength InGaN/GaN MQWs microLED, particularly in wavelength tuning engineering.
Organiser
Professor Anthony Hoi Wai CHOI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Accuracy-Oriented Resource Scheduling for Satellite-Air-Ground Sensing, Computing, and Communication Systems
2026-05-05 14:00 - 15:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
With the evolution of 6G mobile communications, achieving 99.999% seamless global coverage has become a key requirement. Traditional terrestrial networks still suffer from coverage blind spots in scenarios such as emergency rescue and vast ocean areas. Therefore, building a space–air–ground integrated network by combining satellite, aerial, and terrestrial systems has become an important trend. This seminar focuses on mission-driven target recognition within the integrated sensing, communication, and computing (ISCC) framework, emphasizing efficient end-to-end system design under physical constraints. It analyzes the theoretical lower bounds of multiclass classification error probability, including discrimination gain and feature distribution variance, and discusses multi-objective trade-offs and optimization frameworks. The seminar also highlights future research on user-oriented intelligent scheduling for space–air–ground collaborative resources, with the goal of reducing end-to-end latency, improving resource allocation efficiency, and enhancing overall system performance.
Speaker
Mr Zhiyuan XU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Xu Zhiyuan received the B.Sc. degree from Sun Yat-sen University, Guangzhou, China, in 2023. He is currently working toward the M.Phil. degree with The University of Hong Kong, Hong Kong. His research interests include 6G communications.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Novel Two-Dimensional (2D) Memory Devices: From Material Innovation to Functional Integration
2026-05-08 14:30 - 15:30
Zoom Link
https://hku.zoom.us/j/92285676627?pwd=[REDACTED]
Abstract
During the period of the Internet of Things (IoT) and big data, data capacity is growing exponentially, the delay and loss caused by data transmission make the traditional von Neumann computing architecture urgently need to be overturned and restructured. This has led to a growing demand for emerging memory devices, posing significant challenges to conventional silicon-based memory technologies. In recent years, two-dimensional (2D) materials leveraging their unique physical properties, ultra-thin thickness and no dangling bonds have been wide-ranging used in the fabrication of various electronic and optoelectronic memory devices. In this seminar, we first briefly introduce several mainstream types of 2D memory devices developed in recent years and their working mechanisms. We then propose a 2D infrared-sensing memory device (ISMD) based on the Se0.3Te0.7/CuInP2S6 (CIPS) heterostructure, where Se0.3Te0.7 serves as the channel and CIPS functions as the ferroelectric auxiliary layer. The coupling between interfacial defect trapping and ferroelectric polarization endows the device with non-volatile multi-bit memory capability programmable by electrical pulses. Meanwhile, the device exhibits a transient infrared response at 1550 nm, with its responsivity negatively correlated to the channel conductance. By integrating sensing, memory, and computing in a single device, this work broadens the research scope of 2D memory devices.
Speaker
Mr. Xuyang ZHENG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Xuyang Zheng is an MPhil student in the Department of Electrical and Computer Engineering at The University of Hong Kong, supervised by Prof. Can Li. He received his B.S. degree in Functional Materials from South China University of Technology (SCUT) in 2024. His research interests include 2D memory devices for neuromorphic computing.
Organiser
Prof. Can LI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Day–Night Mechanism-Aware Causal Modeling for Wind Power Forecasting: A Physics-Guided NLSEM Framework
2026-05-08 16:00 - 17:00
Zoom Link
https://hku.zoom.us/j/95263844640
Abstract
Wind power forecasting remains highly challenging due to the strong nonlinearity of atmospheric dynamics,pronounced diurnal regime differences, and substantial uncertainties in multi-source meteorological data. Conventional black-box machine-learning models mainly rely on observational correlations, often neglecting physical constraints and causal mechanisms, which leads to limited interpretability and poor robustness under distribution shifts. In this seminar, we proposes a unified forecasting framework that integrates physics-constrained data construction, multi-site causal structure learning, and a global nonlinear structural equation model (NLSEM). The framework combines full-variable Granger causality networks with PCMCI+ to identify distinct day and night-time causal directed acyclic graphs (DAGs). Within the NLSEM, physical monotonicity, environmental invariance, and counterfactual-consistency regularization are explicitly enforced. The resulting model supports causal inference through dointervention analysis, ATE/CATE estimation, and counterfactual reasoning. Experiments conducted on three coastal wind farms demonstrate consistent performance improvements over strong machine-learning baselines, while revealing physically meaningful causal drivers of wind-power generation.
Speaker
Mr. Yuxuan WANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Yuxuan Wang received the B.S. degree in New Energy Science and Engineering from Huazhong University of Science and Technology, and the M.S. degree in electrical engineering from University of Leeds. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the Department of Electrical and Electronic Engineering, The University of Hong Kong. His current research interests include wind power forecasting, causal inference, and nonlinear modeling for renewable energy systems.
Organiser
Prof. Yunhe HOU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.
