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X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260331T140000
DTEND;TZID=Asia/Hong_Kong:20260331T150000
DTSTAMP:20260510T151526
CREATED:20260327T023015Z
LAST-MODIFIED:20260327T023015Z
UID:115430-1774965600-1774969200@ece.hku.hk
SUMMARY:RPG Seminar – Broadband Mamyshev Oscillator at 1.7 μm for Multicolor Three-photon Fluorescence Microscopy
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/95690107722?pwd=KARbsSPDxqYtCaSbRQ0AbxNFmlyBl2.1 \nAbstract\nThree-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. \nSpeaker\nMiss Xiaoxiao Wen\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nXiaoxiao 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. \nOrganiser\nProf. Kenneth Kin-Yip Wong \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260331/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260326T170000
DTEND;TZID=Asia/Hong_Kong:20260326T180000
DTSTAMP:20260510T151526
CREATED:20260320T094513Z
LAST-MODIFIED:20260320T094513Z
UID:115340-1774544400-1774548000@ece.hku.hk
SUMMARY:RPG Seminar – Scaling Up Spatial Awareness: High-Fidelity Data Synthesis for 3D Scene Understanding
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/91627715757?pwd=ByKZvbK3QYx8VSWXVoNGBsZXTpFEz3.1 \nAbstract\nSpatial 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. \nSpeaker\nMr. Jianhui Liu\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMr. 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. \nOrganiser\nProf. Xiaojuan Qi\nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260326/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260324T140000
DTEND;TZID=Asia/Hong_Kong:20260324T150000
DTSTAMP:20260510T151526
CREATED:20260320T093741Z
LAST-MODIFIED:20260320T093741Z
UID:115337-1774360800-1774364400@ece.hku.hk
SUMMARY:RPG Seminar – LLMs for Social Good: Addressing Data Scarcity and Opacity for Alzheimer’s Diagnosis and Prognosis
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/94842355191?pwd=02bHCUfep3119O1jbeDHbnZNKaKUJ8.1 \nAbstract\nEarly 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? \nTo 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. \nSpeaker\nMr. Tingyu MO\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMr. 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. \nOrganiser\nProf. Victor O.K Li\, Prof. Jacqueline C.K Lam\, Prof. Yunhe Hou\nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260324/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260320T090000
DTEND;TZID=Asia/Hong_Kong:20260320T100000
DTSTAMP:20260510T151526
CREATED:20260311T024703Z
LAST-MODIFIED:20260311T024703Z
UID:115299-1773997200-1774000800@ece.hku.hk
SUMMARY:RPG Seminar – Synthetic Aperture for High Spatial Resolution Acoustoelectric Imaging
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/97208194193 \nAbstract\nAcoustoelectric 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. \nSpeaker\nMr. Wei Yi Oon\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nWei 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. \nOrganiser\nProf. Wei-Ning Lee\nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260320/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260319T151500
DTEND;TZID=Asia/Hong_Kong:20260319T161500
DTSTAMP:20260510T151526
CREATED:20260309T094107Z
LAST-MODIFIED:20260309T094107Z
UID:115278-1773933300-1773936900@ece.hku.hk
SUMMARY:Seminar on Integration of Renewable Energy for Power Restoration: Real-time Digital Simulation Approach
DESCRIPTION:Abstract\nThe 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. \nThis 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. \nSpeaker\nDr. Jason Man Hin CHOW\nLecturer at Vocational Training Council (VTC) \nSpeaker’s Biography\nDr. 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.
URL:https://ece.hku.hk/events/20260319-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2026/03/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260317T143000
DTEND;TZID=Asia/Hong_Kong:20260317T153000
DTSTAMP:20260510T151526
CREATED:20260311T063203Z
LAST-MODIFIED:20260311T063203Z
UID:115304-1773757800-1773761400@ece.hku.hk
SUMMARY:Seminar on Why Not Electric Vehicle
DESCRIPTION:Abstract\nThis 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. \nSpeaker\nIr Dr. T. W. CHING\nDepartment of Electrical and Computer Engineering \nSpeaker’s Biography\nIr 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.
URL:https://ece.hku.hk/events/20260317-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2026/03/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260317T100000
DTEND;TZID=Asia/Hong_Kong:20260317T110000
DTSTAMP:20260510T151526
CREATED:20260311T065221Z
LAST-MODIFIED:20260311T081220Z
UID:115308-1773741600-1773745200@ece.hku.hk
SUMMARY:Seminar on An ECE Framework for Instrumentation and Education: From Microscopy Design to Community Outreach
DESCRIPTION:Abstract\nAdvanced 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. \nIn 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. \nSpeaker\nDr. Yu-Chun HSUEH\nResearch Fellow at City University of Hong Kong \nSpeaker’s Biography\nDr. 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.
URL:https://ece.hku.hk/events/20260317-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260306T110000
DTEND;TZID=Asia/Hong_Kong:20260306T120000
DTSTAMP:20260510T151526
CREATED:20260302T022309Z
LAST-MODIFIED:20260302T025720Z
UID:114954-1772794800-1772798400@ece.hku.hk
SUMMARY:Seminar on Cross-Species Functional MRI (fMRI) Investigations of Reinforcement Learning
DESCRIPTION:Abstract\nReinforcement 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. \nIn 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. \nSpeaker\nProf. Bolton KH CHAU\nDepartment of Rehabilitation Sciences\,\nThe Hong Kong Polytechnic University \nSpeaker’s Biography\nProf. 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. \nOrganiser\nDr. Alex Tze Lun LEONG\nDepartment of Electrical and Computer Engineering\,\nThe University of Hong Kong \nAcknowledgement\nTam Wing Fan Innovation Wing Two\n\nAll are welcome!
URL:https://ece.hku.hk/events/20260306-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2026/03/12801.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260210T163000
DTEND;TZID=Asia/Hong_Kong:20260210T180000
DTSTAMP:20260510T151526
CREATED:20260205T035011Z
LAST-MODIFIED:20260205T042115Z
UID:114810-1770741000-1770746400@ece.hku.hk
SUMMARY:Seminar on Exploring the 6G Frontier: Prototyping Key Technologies at Yonsei
DESCRIPTION:Abstract\nThis 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. \nSpeaker\nProf. Chan-Byoung CHAE\nPh.D.\, IEEE Fellow\, NAI Fellow\nUnderwood Distinguished Professor & Lee Youn Jae Fellow\,\nYonsei University\nMember of National Academy of Engineering of Korea\nFormer Editor-in-Chief\, IEEE Trans. MBMC\nIEEE Distinguished Lecturer \nSpeaker’s Biography\nChan-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. \nProf. 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. \nProf. 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). \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20260210-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2026/02/banner1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260206T150000
DTEND;TZID=Asia/Hong_Kong:20260206T180000
DTSTAMP:20260510T151526
CREATED:20260128T021118Z
LAST-MODIFIED:20260128T021118Z
UID:114696-1770390000-1770400800@ece.hku.hk
SUMMARY:Symposium on AI for Social Good: Understanding Energy and Neuro Complex Systems through AI
DESCRIPTION:All EEE MSc students are welcome!
URL:https://ece.hku.hk/events/20260206-1/
LOCATION:Room 601\, 6/F\, MSc Student Commons\, Pacific Plaza\, 410 Des Voeux Road West
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2026/01/345435.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260206T140000
DTEND;TZID=Asia/Hong_Kong:20260206T150000
DTSTAMP:20260510T151526
CREATED:20260129T080556Z
LAST-MODIFIED:20260129T080556Z
UID:114719-1770386400-1770390000@ece.hku.hk
SUMMARY:RPG Seminar – A Hierarchical Predictive Control Approach for Wireless Electric Vehicle Energy Network with Integrated Microgrids Incorporating Degradation Costs
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/2597624634?omn=91226274349 \nAbstract\nIntegrating the wireless electric vehicle energy network with microgrids benefits vehicle owners and grid operators. Yet\, economical and reliable operations for such combinations under renewable energy uncertainties remain examined. To address this\, achieving cost-effective and reliable performance\, a novel hierarchical predictive control approach is utilized. Its core innovation schedules optimal power dispatches for integrated microgrids using different time frames with upper-level control minimizing energy costs and battery energy storage systems’ degradation costs\, whereas lower-level control additionally lowers degradation costs. Moreover\, the approach inherently enhances system reliability by minimizing power fluctuations using control references from upper-level control and state variables feedback from lower-level control under renewable energy uncertainties. The unique cross-time-frame integration of this approach enables modeling and incorporating degradation costs to adapt costs associated with longer time frames into optimal power dispatches in shorter time frames\, reflecting the unique features of hybrid energy storage systems. Comparative studies reveal that various energy storage systems can be employed at different hierarchical control levels for tailored power distribution objectives. Effectiveness of the utilized approach is confirmed by comparing it with control benchmarks through its reduction of energy costs\, degradation costs\, and expected energy not served. \n  \nSpeaker\nMr. Ye Duan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYe DUAN received the B.Eng. degree in mechanical engineering and automation from Chongqing University\, Chongqing\, China\, and an M.Sc. degree in mechanical engineering and applied mechanics from The University of Pennsylvania\, Philadelphia\, USA\, in 2020 and 2022\, respectively. He is currently a PhD candidate in the Department of Electrical and Electronic Engineering at the University of Hong Kong. His main research interests include smart charging for electric vehicles\, energy management and optimization of microgrids\, and wireless power transfer. \nOrganiser\nProf. Yunhe Hou\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260206/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260130T153000
DTEND;TZID=Asia/Hong_Kong:20260130T163000
DTSTAMP:20260510T151526
CREATED:20260121T015143Z
LAST-MODIFIED:20260121T015143Z
UID:114655-1769787000-1769790600@ece.hku.hk
SUMMARY:Seminar on AI Methods for Learning Quantum Systems
DESCRIPTION:Abstract\nAs quantum technologies redefine the landscape of modern electronics and communication networks\, the efficient characterisation and monitoring of complex quantum systems have become essential. In this talk\, I will present AI-driven methods to address the challenges in quantum state learning and property characterisation. First\, I will introduce the Generative Query Network for Quantum (GQNQ)\, which constructs succinct\, data-driven representations from measurement data to accurately predict unseen quantum statistics across diverse quantum systems. I will then discuss a multi-task neural network framework that extracts intricate global properties solely from short-range measurement statistics. Crucially\, our findings demonstrate powerful transfer-learning capabilities\, allowing models trained on small-scale\, classically tractable systems to generalise to much larger quantum systems. These approaches provide a scalable and universal toolkit for optimising next-generation quantum-enhanced electronic and networking systems. \nSpeaker\nDr. Amy Yuexuan WANG \nSpeaker’s Biography\nDr. Amy Yuexuan WANG obtained her Ph.D. from Zhejiang University in 2003. She currently holds a dual appointment as a Senior Research Fellow in the Department of Electrical and Electronic Engineering at The University of Hong Kong (HKU)\, with research expertise in Robotics and Artificial Intelligence\, Wireless Communications and Networking\, Distributed Intelligent Systems and AI for science. \nHer distinguished career includes over 20 years of academic leadership\, with previous roles as a professor at Tsinghua University and as the Associate Director of the AI Lab at HKU. Prof. Wang has an exceptional record in competitive research funding\, having successfully secured and directed numerous major national grants. She has served as the principal investigator for multiple National Key R&D Programs of China and National Natural Science Foundation of China (NSFC) projects\, with a cumulative funding record exceeding RMB 80 million. Her recent applied research focuses on blockchain ecosystem security and privacy-preserving computation\, with technologies deployed in major financial and industrial platforms. \nDr. Wang maintains extensive industry collaborations and holds several patents in areas such as collaborative AI models and 3D printing. Her entrepreneurial work includes co-founding a startup that secured significant contracts for domestically developed AI-CAD software. A recipient of the 2014 National Teaching Achievements Award\, she played a key role in designing Tsinghua University’s pioneering “Yao Class”.  Dr. Wang’s deep technical expertise\, proven leadership in large-scale funded projects\, and strong translational experience will be vital for guiding the project’s technical development and ensuring its practical and commercial impact.
URL:https://ece.hku.hk/events/20260130-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260130T110000
DTEND;TZID=Asia/Hong_Kong:20260130T120000
DTSTAMP:20260510T151526
CREATED:20260123T013325Z
LAST-MODIFIED:20260123T013325Z
UID:114686-1769770800-1769774400@ece.hku.hk
SUMMARY:RPG Seminar – Category Extrapolation for Long-Tail Learning
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/98761635562?pwd=7QQk1kgMNRmNcaNcVr3bsVi0A2LbZs.1#success \nAbstract\nThis paper tackles long-tailed learning where tail classes suffer from poor feature generalization due to limited sample diversity. It makes a key observation—finer-grained datasets are less harmed by class imbalance—and supports it with quantitative and qualitative evidence showing that increasing granularity improves tail-category feature generalization. Motivated by this\, the authors propose Category Extrapolation: they augment the dataset with open-set\, fine-grained auxiliary classes related to existing categories to strengthen representation learning for both head and tail classes. To automate auxiliary data collection\, they use LLMs as a knowledge base to discover related categories and web crawling to retrieve images. To prevent auxiliary classes from dominating training\, they introduce a neighbor-silencing loss that keeps the model focused on discriminating target classes; at inference time\, auxiliary classifier weights are masked out\, using only the target classes. \nSpeaker\nMr. Shizhen Zhao\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nShizhen Zhao is a PhD student in the Department of Electrical and Electronic Engineering at The University of Hong Kong (HKU). He received the M.S. degree from Huazhong University of Science and Technology and the B.S. degree from Wuhan University of Technology. His research focuses on computer vision\, with particular interests in open-world perception\, long-tail and few-shot learning\, and out-of-distribution (OOD) detection. \nOrganiser\nProf. Xiaojuan Qi\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260130/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260130T100000
DTEND;TZID=Asia/Hong_Kong:20260130T110000
DTSTAMP:20260510T151526
CREATED:20260126T040445Z
LAST-MODIFIED:20260126T040445Z
UID:114690-1769767200-1769770800@ece.hku.hk
SUMMARY:RPG Seminar – New Paradigm for Universal Graph Prompt Tuning
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/92120929746?pwd=aL2f7KSR1uAb9rKfYOyaxpOKa9SBQL.1 \nAbstract\nWe strengthen the theoretical foundation of universal graph prompt tuning by introducing stricter constraints\, demonstrating that adding prompts to all nodes is a necessary condition for achieving the universality of graph prompts. To this end\, we propose a novel model and paradigm\, Learning and Editing Universal GrAph Prompt Tuning (LEAP)\, which preserves the theoretical foundation of universal graph prompt tuning while pursuing more ideal prompts. Specifically\, we first build the basic universal graph prompts to preserve the theoretical foundation and then employ actor-critic reinforcement learning to select nodes and edit prompts. Extensive experiments on graph- and node-level tasks across various pre-training strategies in both full-shot and few-shot scenarios show that LEAP consistently outperforms fine-tuning and other prompt-based approaches. \nSpeaker\nMr. Jinfeng Xu\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nJinfeng Xu is a Ph.D. candidate in the Department of Electrical and Electronic Engineering at The University of Hong Kong\, under the supervision of Prof. Edith C. H. Ngai. His current research interests include Recommendation System\, Data Privacy\, Graph Learning\, Self-supervised Learning\, Computer Vision\, and Federated Learning. \nOrganiser\nProf. Edith C. H. Ngai\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260130-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260129T110000
DTEND;TZID=Asia/Hong_Kong:20260129T120000
DTSTAMP:20260510T151526
CREATED:20260119T015543Z
LAST-MODIFIED:20260119T015543Z
UID:114612-1769684400-1769688000@ece.hku.hk
SUMMARY:Seminar on Distributed Optimisation Frameworks for Large-Scale Nonlinear Programming in Power Systems
DESCRIPTION:Abstract\nThe ongoing energy transition is challenging centralised power system paradigms by rapidly integrating distributed energy resources (DERs)\, which introduce significant supply-demand variability. This variability complicates grid management and necessitates enhanced coordination among operators. Centralised data aggregation further exacerbates privacy risks and strains the communication infrastructure as the proliferation of controllable devices increases.\nTo address these challenges\, this presentation introduces advances in distributed frameworks for nonconvex nonlinear programming (NLP). The first approach refines a distributed Sequential Quadratic Programming (SQP) framework that integrates the barrier method and Schur-complement-based communication reduction\, enabling efficient parallelisation through graph decomposition. Large-scale AC optimal power flow (OPF) benchmarks demonstrate its superiority over the centralised solver IPOPT. The framework solves problems with over 500\,000 variables at speeds 2–8 times faster than IPOPT on standard workstations while maintaining numerical robustness. The second approach leverages the hierarchical structure of integrated transmission–distribution (ITD) systems and casts coordination as a non-iterative\, two-layer optimisation scheme. By communicating aggregated distribution-level flexibility to the transmission layer\, the method eliminates the need for detailed distribution-network models in system-level coordination. Simulations under severe weather conditions in Germany demonstrate robustness to prediction errors and underscore the scalability and privacy-preserving properties of the proposed strategy. \nSpeaker\nDr. Xinliang DAI\nPostdoctoral Research Associate\,\nPrinceton University \nSpeaker’s Biography\nDr. Xinliang DAI received the B.Sc. degree from Jilin University\, China\, and the M.Sc. and Ph.D. degrees from the Karlsruhe Institute of Technology (KIT)\, Germany. He is currently a Postdoctoral Research Associate with the Zero-carbon Energy Systems Research and Optimisation Laboratory (ZERO Lab) at Princeton University\, USA. His research interests include graph-based distributed optimisation\, flexibility aggregation\, and GPU acceleration for large-scale optimisation. \nOrganiser\nProfessor Tao LIU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20260129-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260127T150000
DTEND;TZID=Asia/Hong_Kong:20260127T163000
DTSTAMP:20260510T151526
CREATED:20260122T101148Z
LAST-MODIFIED:20260122T101353Z
UID:114681-1769526000-1769531400@ece.hku.hk
SUMMARY:Seminar on Approaches to Modelling and Analysis of Sustainable Power Networks
DESCRIPTION:Abstract\nDue to the evident climate change and environmental pressures the future power/energy systems will have to operate\, sooner rather than later\, in a net-zero environment. This will manifest in a mix of wide range of electricity generation\, storage and demand technologies (increasingly power electronics interfaced); blurred boundaries between transmission and distribution system; significantly higher reliance on the use of legacy and measurement data including global signals for system identification\, characterisation\, and control and Information and Communication Technology embedded within the power system network and its components. The key characteristics of such a complex system\, would certainly be proliferation of power electronic devices in different shapes and forms and for different purposes\, increased uncertainties in system operation and parameters and much larger reliance on the use of measurement and other data collected. \nThis presentation will first briefly introduce some of the key characteristics of net-zero power systems characterised by high proliferation of power electronics (PE) based transmission and control devices/technologies and power electronics connected low carbon technologies (generation\, demand and storage). It will then discuss the control and operation advantages that introduction of these technologies offers\, reflect on resulting challenges that their introduction in the power system poses and elaborate on the need for holistic (multi parameter\, multi criteria and multi system) approach to solving the identified challenges. \nThis will be followed by illustrative examples of both\, advantages and disadvantages resulting from proliferation of power electronics based technologies on power system’s steady state and dynamic performance and examples of a holistic approach to multicriteria multi system analysis of net zero power systems focusing on applications of nondeterministic approaches\, use of data analytics and machine learning. \nThe presentation will conclude with recommendations for overcoming the identified challenges and harnessing the full potential of power electronics based or interfaced technologies for transition to net-zero power systems in foreseeable future. \nSpeaker\nProf. Jovica V MILANOVIĆ\nFormer Head of Department (Dean)\,\nElectrical and Electronic Engineering\,\nThe University of Manchester \nSpeaker’s Biography\nProf. Jovica V MILANOVIĆ received Dipl.Ing. and M.Sc. degrees from the University of Belgrade\, Yugoslavia\, Ph.D. degree from the University of Newcastle\, Australia\, and D.Sc. degree from The University of Manchester\, UK. Prior to joining The University of Manchester\, UK\, in 1998\, he worked with “Energoproject”\,\nEngineering and Consulting Co. and the University of Belgrade in Yugoslavia\, and the Universities of Newcastle and Tasmania in Australia. \nProf. Milanović is immediate past Head of Department (Dean) of Electrical and Electronic Engineering at The University of Manchester\, UK\, Visiting Professor at the University of Novi Sad and the University of Belgrade\, Serbia and Honorary Professor at the University of Queensland\, Australia. \nHe was chairman of 6 international conferences\, member of 9 (convenor of 3) past IEEE/CIGRE/CIRED WG\, participated in or lead numerous research projects with total value of over £86 million\, published over 650 research papers and reports\, gave over 35 key-note speeches at international conferences and presented over 150 courses/tutorials and lectures to industry and academia around the world. In addition to his academic work\, he has been or is a consultant for various international companies including\, Member of the Rolls-Royce Plc. Executive Advisory Board (Research and Technology – Energy: Electrical\, Control Systems & Electronics)\, UK\, Member of the Electricity North West Ltd. Customer Engagement Group\, Member of the Independent Net Zero Advisory Council\, Scottish Power Energy Networks\, UK\, Member of the International Advisory Board\, El. Eng. Institute “Nikola Tesla”\, Belgrade\, Serbia and Member of Board of Directors (non-executive Director) of Montenegrin Electric Enterprise AD Niksic\, Montenegro. \nProf. Milanovic is Fellow of the Royal Academy of Engineering (UK)\, Foreign member of the Serbian Academy of Engineering Sciences\, Fellow of the IEEE\, Fellow of the IET Chartered Engineer in the UK and Distinguished IEEE PES Lecturer. He is a member of IEEE PES Governing board\, Executive Board and Financial Committee\, IEEE PES Long Range Planning Committee and IEEE PES Vice President – Publications. He was a member of the IEEE PES Governing Board as Regional Representative for Europe\, Middle East and Africa for six years\, member and vice-chair of IEEE PES Fellows Evaluation Committee\, Chair of the IEEE Herman Halperin Transmission and Distribution Award Committee\, member of the IEEE Fellows Committee and immediate past Editor-in-Chief of IEEE Transactions on Power Systems. \nOrganiser\nProf. Yunhe HOU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20260127-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260120T100000
DTEND;TZID=Asia/Hong_Kong:20260120T113000
DTSTAMP:20260510T151526
CREATED:20260112T102130Z
LAST-MODIFIED:20260113T011347Z
UID:114591-1768903200-1768908600@ece.hku.hk
SUMMARY:Seminar on Advanced Solid-State Laser Technology and Its Applications
DESCRIPTION:*After the seminar\, you are also welcome to attend in-person discussions with the speakers from 11:30 am to 12:30 pm in Room 601J. \nAbstract\nSolid-state lasers mean laser systems using solid-state materials as the gain medium\, primarily including rod\, slab\, disc\, fiber\, and semiconductor lasers. These methods offer advantages such as high output power\, excellent electro-optical efficiency\, broad wavelength coverage\, wide pulse modulation range\, compact size\, high stability and reliability\, user-friendly operation\, and low comprehensive cost. They have been widely applied in fields such as advanced manufacturing\, electronics\, biochemistry\, medical applications\, and scientific research\, accounting for up to 84% of current laser equipment. This report will focus on the characteristics of high-power solid-state lasers\, global research advancements\, and the major achievements in high-power solid-state laser technology and applications made by the Laser Center at the Institute of Physics and Chemistry\, Chinese Academy of Sciences (TIPC\, CAS). The key achievements include ten-kilowatt-level near-infrared lasers\, kilowatt-level green lasers\, hundred-watt-level sodium beacon lasers\, hundred-watt-level ultraviolet\, and deep ultraviolet lasers\, which could be applied in fields such as laser precision machining\, detection imaging\, advanced medical treatments\, and cutting-edge scientific exploration. \nSpeaker\nProf. Yong BO & Prof. Xiaoyong GUO\nInstitute of Physics and Chemistry (TIPC)\,\nChinese Academy of Sciences (CAS) \nSpeakers’ Biography\nProf. Yong BO was born in Feb. 1972 and held Ph.D. in Engineering. He currently serves as a Research Fellow and Ph.D. Supervisor at the Institute of Physics and Chemistry (TIPC)\, Chinese Academy of Sciences (CAS). He obtained his Ph.D. from Tsinghua University in 2003 and worked at the Institute of Physics\, CAS from 2003 to 2008. Since 2008\, he has always been working at TIPC\, CAS. His primary research focuses on high-power solid-state lasers with the frequency conversion technologies. He has achieved many world- advanced research results include ten-kilowatt-level near-infrared solid-state lasers\, kilowatt-level green solid-state lasers\, hundred-watt-level ultraviolet solid-state lasers\, and hundred-watt-level sodium beacon solid-state lasers\, which are applied in fields such as laser precision machining\, detection imaging\, and laser medicine. He has been awarded the Second Class Award of the National Technological Invention Award in 2017 and the Beijing Science and Technology Award in 2015. He has published over 200 papers and obtained more than 50 invention patents. \nProf. Xiaoyong GUO was born in Feb. 1974 and held a Ph.D. in Science. He currently serves as a Research Fellow\, Ph.D. Supervisor\, and Deputy Director at the Institute of Physics and Chemistry (TIPC)\, Chinese Academy of Sciences (CAS). Concurrently\, He also holds positions as the Deputy Director of the National Key Laboratory of Light Turbine Power\, head of the CAS Expert Group for Specialized Fields\, and leader or member of multiple national-level expert groups. With extensive experience in optical and mass spectrometry research as well as strategic high-tech management\, he has served as chief or deputy chief commander for numerous national initiatives and led multiple national and provincial-level research projects. His accolades include one national-level award and two provincial-and-ministerial-level awards. He has published dozens of academic papers and three monographs. \nOrganisers\nProf. Chao XIANG & Prof. Xianhao CHEN\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong
URL:https://ece.hku.hk/events/20260120-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260114T140000
DTEND;TZID=Asia/Hong_Kong:20260114T150000
DTSTAMP:20260510T151526
CREATED:20251209T084053Z
LAST-MODIFIED:20251209T084053Z
UID:114360-1768399200-1768402800@ece.hku.hk
SUMMARY:Seminar on Quantum Dot Nanocrystal Based Optoelectronic Devices and Infrared Image Sensors
DESCRIPTION:Abstract\nColloidal semiconductor quantum dot nanocrystals (QD NCs) have been shown to be promising materials for electronic and optoelectronic device applications because of their unique size dependent properties and solution processability. We develop a systematic methods to engineer the surface chemistry of quantum dot nanocrystals to control the charge carrier statistics as well as optical properties. We fabricate QD based infrared photodetectors (PDs) by examining and modifying the charge carrier transport and injection and by designing the structures of the nanocrystal based devices. Combinational studies have been conducted to improve the responsivity\, linear dynamic range\, noise\, detectivity of the PDs. A new patterning method is introduced by engineering the surface states of QDs\, achieving the high resolution devices. Newly developed patterning method is compatible with conventional photolithography process based on all‐solution processes. We introduce the pixel based and pixel-less image sensors with photomultiplication process in QD thin films. Finally\, the strategy to incorporate various NCs and QDs into multi-functional devices and system is discussed. \nSpeaker\nProf. Soong Ju OH\nDepartment of Materials Science and Engineering\,\nKorea University \nSpeaker’s Biography\nProf. Soong Ju OH obtained B.S. degree in Materials Science and Engineering from Korea University in 2007\, and received his Ph.D degree in Materials Science and Engineering from the University of Pennsylvania. He worked in KIST from 2007 to 2008\, and worked as a postdoctoral researcher at the University of Illinois at Urbana Champaign from 2014 to 2015. He joined the faculty of Korea University in 2015\, and is now a full professor of Materials Science and Engineering at Korea University. His current research interests include quantum dot and nanocrystal based electronic and optoelectronic devices\, and multifunctional\, image and wearable sensors. \nOrganiser\nProf. Leo Tianshuo ZHAO\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20260114-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260107T140000
DTEND;TZID=Asia/Hong_Kong:20260107T150000
DTSTAMP:20260510T151526
CREATED:20251218T015218Z
LAST-MODIFIED:20251218T015218Z
UID:114477-1767794400-1767798000@ece.hku.hk
SUMMARY:RPG Seminar – Incentive Mechanism Design for Split Federated Learning
DESCRIPTION:Abstract\nSplit Federated Learning (SFL) combines computational efficiency with privacy preservation\, but existing incentive mechanisms assume uniform cut-layer configurations across clients. This seminar presents a novel incentive mechanism that accounts for heterogeneous cut layer selections in SFL systems. \nWe formulate the problem as a Stackelberg game and derive theoretical convergence guarantees. The proposed mechanism provably achieves equilibrium with desirable strategic properties. Experimental results demonstrate significant improvements in participation rates\, model accuracy\, and resource efficiency compared to existing approaches. \nSpeaker\nMs. Xi Lin\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMs. Xi Lin is an Mphil student in the Department of Electrical and Electronic Engineering at The University of Hong Kong\, supervised by Prof. Xianhao Chen. She received her B.Eng. degree from Shandong University\, China\, in 2024. Her research interests include split federated learning\, incentive mechanism design\, and distributed machine learning systems. \nOrganiser\nProf. Xianhao Chen\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260107/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260106T143000
DTEND;TZID=Asia/Hong_Kong:20260106T153000
DTSTAMP:20260510T151526
CREATED:20251204T020220Z
LAST-MODIFIED:20251204T020220Z
UID:114335-1767709800-1767713400@ece.hku.hk
SUMMARY:Seminar on Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximisation
DESCRIPTION:Abstract\nIn this talk\, we will introduce SubLoRA\, a rank determination method for Low-Rank Adaptation (LoRA) based on submodular function maximisation. In contrast to prior approaches\, such as AdaLoRA\, that rely on first-order (linearised) approximations of the loss function\, SubLoRA utilises second-order information to capture the potentially complex loss landscape by incorporating the Hessian matrix. We show that the linearization becomes inaccurate and ill-conditioned when the LoRA parameters have been well optimised\, motivating the need for a more reliable and nuanced second-order formulation. To this end\, we reformulate the rank determination problem as a combinatorial optimisation problem with a quadratic objective. However\, solving this problem exactly is NP-hard in general. To overcome the computational challenge\, we introduce a submodular function maximisation framework and devise a greedy algorithm with approximation guarantees. We derive a sufficient and necessary condition under which the rank-determination objective becomes submodular\, and construct a closed-form projection of the Hessian matrix that satisfies this condition while maintaining computational efficiency. Our method combines solid theoretical foundations\, second-order accuracy\, and practical computational efficiency. We further extend SubLoRA to a joint optimisation setting\, alternating between LoRA parameter updates and rank determination under a rank budget constraint. Extensive experiments on fine-tuning physics-informed neural networks (PINNs) for solving partial differential equations (PDEs) demonstrate the effectiveness of our approach. Results show that SubLoRA outperforms existing methods in both rank determination and joint training performance. \nSpeaker\nDr. Yihang GAO\nDepartment of Mathematics\,\nNational University of Singapore (NUS)\, Singapore \nSpeaker’s Biography\nYihang GAO is currently a Research Fellow in the Department of Mathematics at the National University of Singapore (NUS)\, Singapore. He received the B.S. degree in Mathematics and Applied Mathematics from Zhejiang University\, China\, in 2020\, and the Ph.D. degree in Mathematics from The University of Hong Kong (HKU)\, Hong Kong SAR\, in 2024. His research interests include mathematical machine learning\, optimisation\, and scientific computing. \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20260106-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260106T110000
DTEND;TZID=Asia/Hong_Kong:20260106T120000
DTSTAMP:20260510T151526
CREATED:20251205T070843Z
LAST-MODIFIED:20251205T070843Z
UID:114342-1767697200-1767700800@ece.hku.hk
SUMMARY:Seminar on Machine Learning\, Artificial Intelligence\, Neuro Imaging Focusing on Longevity and Dementia (MANIFOLD)
DESCRIPTION:Abstract\nBrain health is one of the key societal challenges for the 21st century\, and much progress has been made in understanding and treating brain health conditions\, aided by growing use of neuroimaging. Meanwhile\, artificial intelligence and machine learning (AI/ML) technologies have revolutionised many domains\, including healthcare. However\, there is still a translational gap between AI/ML methods and the use of neuroimaging to detect\, treat and care for people with neurodegenerative or neurodevelopmental conditions. My talk will provide an overview of the research of the MANIFOLD lab at UCL\, that aims to bridge this gap and provide clinically useful neuroimaging tools to improve brain health. I will focus on methods that emphasise the individual patient\, namely the brain-age paradigm and neuroanatomical normative modelling\, applied to Alzheimer’s disease and dementia with Lewy bodies and frontotemporal dementia. Beyond this\, I will talk about our research in explainable AI (XAI)\, AI/ML data fusion\, automated ML and accessible MRI using portable scanners and how we have or plan to apply these in studies of brain diseases. \nSpeaker\nProf. James COLE\nProfessor of Neuroimage Computing\,\nUCL Hawkes Institute and the Dementia Research Centre (DRC)\,\nUniversity College London (UCL) \nSpeaker’s Biography\nJames Cole is Professor of Neuroimage Computing at the UCL Hawkes Institute and the Dementia Research Centre (DRC) at University College London (UCL). His research interests include brain ageing\, neurological and psychiatric diseases\, with a particular focus on ageing\, neurodegeneration and dementia. His work uses machine learning\, deep learning and related statistical methods with the goal of developing clinically useful neuroimaging tools. He is Principal Investigator of the MANIFOLD Lab. \nOrganiser\nProf. Ed Xuekui WU\nChair of Biomedical Engineering\,\nLam Woo Professorship in Biomedical Engineering\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAcknowledgement\nTam Wing Fan Innovation Wing Two\n\nAll are welcome!
URL:https://ece.hku.hk/events/20260106-2/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251223T143000
DTEND;TZID=Asia/Hong_Kong:20251223T153000
DTSTAMP:20260510T151526
CREATED:20251219T110207Z
LAST-MODIFIED:20251219T110610Z
UID:114486-1766500200-1766503800@ece.hku.hk
SUMMARY:RPG Seminar – Design of Novel Structured Light Beams in Optical Manipulation and Two-Photon Microscopy
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/6983793721?pwd=UjwaYARlhjvhUf7DDp3bMjbJnlhVbr.1 \nAbstract\nStructured illumination refers to a class of optical imaging methods in which the sample is illuminated with a controlled spatial light pattern rather than uniform illumination. By altering the illumination pattern\, it can facilitate the extraction of spatial or temporal information from the sample\, and may improve imaging performance such as resolution or acquisition speed. It also enables precise manipulation of microscopic objects through targeted light patterns. One example is the higher-order BBs generated by adding a vortex phase to the zero-order BBs and exhibiting a ring-shaped intensity distribution. Their characteristic length results in different trapping states of particles with different sizes\, which is of assistance to the size-sorting process. To this date\, there has been no research on high-order conveyor beams with a vortex modulation. In this study\, a spatial light modulator (SLM) is used to generate the ring-shaped conveyor beams. The beam profile is simulated and then confirmed with a two-photon microscopy system. The conveyor beam shows two alternating rings with an axial period. This technique opens a door to simultaneous optical manipulation of the particle’s OAM and longitudinal translation. The application of another class of beams\, axially encoded beams\, in multiphoton microscopy (MPM)\, will also be introduced. Combined with computational decoding\, these beams enable axially parallel excitation (APEX) that extends per-plane exposure without slowing acquisition or requiring higher power. High-speed volumetric MPM with improved contrast will be demonstrated. \nSpeaker\nMs. Minghui SHI\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nSpeaker’s Biography\nMinghui SHI received the B.S. degree in optical information science and technology from the Beijing University of Technology\, Beijing\, China\, in 2021. She is currently working toward the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. Her research interests include structured illumination\, fluorescence imaging\, and mid-infrared laser applications. \nOrganiser\nProf. Kenneth K.Y. WONG\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20251223-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251223T143000
DTEND;TZID=Asia/Hong_Kong:20251223T153000
DTSTAMP:20260510T151526
CREATED:20251217T024739Z
LAST-MODIFIED:20251217T024739Z
UID:114432-1766500200-1766503800@ece.hku.hk
SUMMARY:RPG Seminar – Design of High-Performance and Energy-Efficient AI Accelerator for Edge Computing
DESCRIPTION:Abstract\nThis seminar provides a systematic showcase of our end-to-end energy-efficient solutions in AI accelerator design\, spanning from the foundational compute unit to specialized architectures and large model deployment on edge devices. We begin by introducing a novel Reconfigurable Processing Element (PE)\, the core compute unit designed to support multiple floating-point (e.g.\, BF16\, FP16) and fixed-point (e.g.\, INT8\, INT4) Multiply-Accumulate (MAC) operations with 100% hardware utilization\, achieving exceptional energy efficiency exceeding 1700 GFLOPS/W for deep learning tasks. Subsequently\, addressing the need for extreme efficiency in edge vision\, we present dedicated\, multiplication-free Look-Up Table (LUT) accelerators like BDLUT (for blind denoising)\, EdgeLUT (for all fixed-resolution image restoration) and ScaleLUT (for real-time 4K super-resolution). These designs replace traditional convolutional operations with efficient LUT inference\, resulting in significantly lower hardware and power consumption. Finally\, we focus on high-efficiency acceleration for complex modern AI models: we propose the QuadINR framework\, which utilizes hardware-efficient piecewise quadratic activation functions for Implicit Neural Representations (INR). For Large Language Models (LLMs)\, EdgeLLM is a highly efficient CPU-FPGA heterogeneous accelerator that employs a mixed-precision PE array (FP16/FP16 and FP16/INT4) and a unified data parallelism scheme\, successfully tackling LLM deployment challenges to achieve up to 1.91x higher throughput and 7.55x better energy efficiency than commercial GPUs. In summary\, this presentation offers a comprehensive view of our complete technological roadmap in the world of efficient AI chips\, achieved through co-optimization of hardware and algorithms\, to realize high-performance and energy-efficient AI acceleration for edge computing. \nSpeaker\nMr. Boyu Li\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nBoyu Li is a Ph.D. candidate in the Department of Electrical and Electronic Engineering at The University of Hong Kong\, under the supervision of Professor Ngai Wong. His research focuses on deep learning\, reconfigurable computing\, and the design of AI accelerators. \nOrganiser\nProf. Ngai Wong\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251223/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251219T150000
DTEND;TZID=Asia/Hong_Kong:20251219T163000
DTSTAMP:20260510T151526
CREATED:20251216T014340Z
LAST-MODIFIED:20251216T014340Z
UID:114428-1766156400-1766161800@ece.hku.hk
SUMMARY:Seminar on An Update on Machine Learning for Communication Networks
DESCRIPTION:Abstract\nThe speaker aims to provide an update on recent progress by his research team on machine learning for communication networks. If time permits\, he will also highlight his work on distributed quantum computing and quantum machine learning. \nEfficient allocation of limited resources to competing demands is a crucial issue in the design and management of communication networks. In this seminar\, the speaker will first introduce a new reinforcement-learning (RL) technique for achieving optimal resource allocation in networks with periodic traffic patterns. The effectiveness of this method will be demonstrated through numerical examples. \nIn addition\, a new RL technique will be presented that separates representation learning from RL to enable fully decentralised learning in partially observable multi-agent settings. The approach relies on learned beliefs over the underlying system state. A belief model is first trained by using complete environment information\, which is then used by a state-based RL algorithm using distributed\, local observations only. A set of partially observable environments is constructed\, and the efficacy of this new approach is shown and compared to relevant benchmarks. \nIf time permits\, the speaker will also highlight his recent work on distributed quantum computing and quantum machine learning. \nSpeaker\nProf. Kin K. LEUNG\nDepartment of Electrical and Electronic Engineering\,\nDepartment of Computing\,\nImperial College\, London \nSpeaker’s Biography\nKin K. LEUNG received his B.S. degree from the Chinese University of Hong Kong\, and the M.S. and Ph.D. degrees from University of California\, Los Angeles. He worked at AT&T Bell Labs and its successor companies in New Jersey from 1986 to 2004. Since then\, he has been the Tanaka Chair Professor at Imperial College in London. He was the Head of Communications and Signal Processing Group from 2019 to 2024 and now serves as Co-Director of the School of Convergence Science: Space\, Security and Telecommunications at Imperial. His current research focuses on optimisation and machine learning for design and control of large-scale communications\, computer and quantum networks. He also works on multi-antenna and cross-layer designs for wireless networks. \nHe is a Fellow of the Royal Academy of Engineering\, IEEE Fellow\, IET Fellow\, and member of Academia Europaea. He received the Distinguished Member of Technical Staff Award from AT&T Bell Labs (1994) and the Royal Society Wolfson Research Merits Award (2004-09). Jointly with his collaborators\, he received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize (2021)\, the IEEE ComSoc Best Survey Paper Award (2022)\, the U.S.–UK Science and Technology Stocktake Award (2021)\, the Lanchester Prize Honorable Mention Award (1997)\, and several best conference paper awards. He chaired the IEEE Fellow Evaluation Committee for ComSoc (2012-15) and served as the General Chair of the IEEE INFOCOM 2025. He has served as an editor for 10 IEEE and ACM journals and chaired the Steering Committee for the IEEE Transactions on Mobile Computing. Currently\, he is an editor for the ACM Computing Survey and International Journal of Sensor Networks. \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20251219-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251219T110000
DTEND;TZID=Asia/Hong_Kong:20251219T120000
DTSTAMP:20260510T151526
CREATED:20251215T072356Z
LAST-MODIFIED:20251215T072356Z
UID:114422-1766142000-1766145600@ece.hku.hk
SUMMARY:RPG Seminar – Towards End-to-End Visual Generation: from Matching to Evaluation
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/91953486299?pwd=lEW6N6JKhmAHLoS38VohufbEoJyJtw.1 \nAbstract\nThis seminar examines how modern image generation models can be redesigned to achieve both high fidelity and high efficiency under flexible inference budgets. I will begin with a brief overview of generative modeling\, focusing on diffusion models and related flow-based formulations. While these approaches deliver strong stability and visual quality\, they typically rely on iterative reverse-time sampling and therefore require many sequential steps at inference\, which limits practicality in latency- or compute-constrained settings. \nNext\, I will discuss mainstream strategies for reducing inference steps—such as distillation and consistency-style objectives—and highlight their common limitations\, including reliance on strong pretrained teachers or difficulties in stable training from scratch at scale. Building on this\, I will introduce an end-to-end perspective that unifies matching and evaluation within a single training framework. In particular\, I will present the idea of self-evaluation as a training signal: the model learns from data-driven local supervision while simultaneously assessing its own generated samples using its current estimates\, effectively acting as a dynamic self-teacher. This coupling bridges local learning and global distribution alignment\, enabling any-step text-to-image inference that degrades gracefully with fewer steps and improves monotonically as more steps are allocated. \nSpeaker\nMr. Xin Yu\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMr. Xin Yu is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, supervised by Professor Xiaojuan Qi. He received his bachelor’s degree in Mathematics and Applied Mathematics from Sun Yat-sen University in 2021. His research interests primarily lie in generative models for computer vision. \nOrganiser\nProf. Xiaojuan Qi\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251219/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251217T110000
DTEND;TZID=Asia/Hong_Kong:20251217T120000
DTSTAMP:20260510T151526
CREATED:20251211T021448Z
LAST-MODIFIED:20251211T021448Z
UID:114396-1765969200-1765972800@ece.hku.hk
SUMMARY:RPG Seminar – Bridging Visual Generation and Understanding in Native MLLMs with a Unified Visual Tokenizer
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/98499142544?pwd=zvVs3BqWzIzCA071Dqq2rYW7vIAqj7.1 \nAbstract\nThe advent of GPT-4o highlights the immense potential of Multimodal Large Language Models (MLLMs) with native visual generation capabilities. These unified models offer precise control in multimodal interactions\, enabling exceptional fluency in tasks such as multi-turn image editing and visual in-context learning. However\, a fundamental dilemma remains in the choice of visual tokenizers for unified MLLMs – e.g.\, semantic tokenizers like CLIP excel in multimodal understanding but complicates generative modeling due to its high-dimensional\, continuous feature space; Conversely\, VQVAE tokenizers fit autoregressive generation but struggles to capture essential semantics for understanding. \nThis seminar explores how to design a unified visual tokenizer to bridge the gap in multimodal generation and understanding. Recent studies attempt to address this by connecting the training of VQVAE (for autoregressive generation) and CLIP (for understanding). However\, directly combining these training objectives has been observed to cause severe loss conflicts.  We will show that reconstruction and semantic supervision do not inherently conflict. Instead\, the underlying bottleneck stems from limited representational capacity of discrete token space. Building on these insights\, we introduce UniTok\, a unified tokenizer featuring a novel multi-codebook quantization mechanism that effectively scales up the vocabulary size and bottleneck dimension. \nSpeaker\nMr. Chuofan Ma\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMr. Chuofan Ma is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, supervised by Professor Xiaojuan Qi. He received his bachelor’s degree in computer science from The University of Hong Kong in 2021. His research interests primarily lie in open-world visual intelligence and multi-modal foundation models. \nOrganiser\nProf. Xiaojuan Qi\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251217/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251217T100000
DTEND;TZID=Asia/Hong_Kong:20251217T110000
DTSTAMP:20260510T151526
CREATED:20251215T071917Z
LAST-MODIFIED:20251215T072002Z
UID:114421-1765965600-1765969200@ece.hku.hk
SUMMARY:Seminar on Efficient Generative Modelling\, Multi-agent Systems Based on Knowledge Graphs and LLMs
DESCRIPTION:Abstract\nI will overview our recent results on diffusion generative modelling and how to make inference faster\, just in a few steps; also\, I will provide some new concepts of Engineering AI and discuss how we can construct efficient multi-agent systems based on knowledge graphs and LLMs to solve complex engineering problems. \nSpeaker\nProf. Evgeny BURNAEV\nVice President for AI Development & Professor\,\nSkolkovo Institute of Science and Technology\nVisiting Chair Professor\,\nHarbin Institute of Technology \nSpeaker’s Biography\nEvgeny BURNAEV is Vice President for AI Development and Professor at the Skolkovo Institute of Science and Technology (Skoltech)\, where he also directs the Skoltech AI Center. His research focuses on engineering AI\, generative modelling\, optimal transport\, physics-informed machine learning\, and topological data analysis for reliable\, efficient\, and interpretable AI systems. At the AI Center\, Burnaev leads interdisciplinary projects that bridge theoretical foundations and large-scale applications in energy\, transport\, materials\, and climate modelling. \nHe has authored more than 200 peer-reviewed publications in leading international venues (NeurIPS\, ICML\, ICLR\, IEEE\, Nature Scientific Reports) and collaborates with global industry leaders such as Sber\, Huawei\, and Gazprom Neft. His achievements have been recognised with the Russian Government Prize in Science and Technology (2024)\, the Sber Science Award (2024)\, and inclusion in the Elsevier–Stanford global Top-2% scientists list (2023–2025). He also serves as Visiting Chair Professor at the Harbin Institute of Technology and contributes to international expert communities and program committees advancing transparent and trustworthy AI worldwide. \nOrganiser\nProf. Ngai WONG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20251217-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251216T141500
DTEND;TZID=Asia/Hong_Kong:20251216T151500
DTSTAMP:20260510T151526
CREATED:20251213T025301Z
LAST-MODIFIED:20251213T025434Z
UID:114410-1765894500-1765898100@ece.hku.hk
SUMMARY:Seminar on Developing Value-driven AI: Building Large Language Models with Social Good Principles
DESCRIPTION:Abstract\nThis talk highlights the critical need and urgency for academic communities to advance artificial intelligence (AI) with a focus on value-driven and socially-beneficial LLMs. The presentation is structured in two parts. First\, I will briefly outline my academic and research background\, our vision for AI for Social Good\, and key contributions from over a decade of work in this field. The second part will focus on the development of a large language model (LLM) system embedded with social good principles. As LLMs\, like ChatGPT\, become integral to daily life\, understanding and addressing their ethical and social implications is paramount. This talk explores how implicit values in AI systems can be identified and reshaped using techniques such as fine-tuning and data generation to align with inclusive\, responsible\, and ethical standards. By embedding societal values into LLM design\, this work aims to foster AI systems that promote fairness\, accountability\, and positive societal impact. The significance of this talk lies in its potential to inspire HKU to prioritize ethical AI development\, shaping a future where AI serves as an accelerator for social good. \nSpeaker\nProf. Jacqueline C.K. LAM\nAssociate Professor\,\nDepartment of Electrical and Electronic Engineering (EEE)\,\nThe University of Hong Kong (HKU) \nSpeaker’s Biography\nProf. Jacqueline C.K. LAM is an Associate Professor in the Department of Electrical and Electronic Engineering (EEE) at The University of Hong Kong (HKU)\, where she co-leads the HKU-AI to Advance Well-being and Society Research Lab. With a PhD in Environmental Management from HKU’s Faculty of Architecture (2008)\, she earned a competitive university-wide Research Assistant Professorship based in EEE\, HKU in 2011\, enabling her to pursue interdisciplinary research integrating data science\, social sciences\, neuroscience\, and ethics. Prof. Lam champions AI for Social Good (AIfSG)\, her research places priority on addressing societal challenges\, particularly in air pollution\, asthma and Alzheimer’s disease\, emphasizing fairness\, explainability\, through big data and AIfSG technologies. \nProf. Lam co-leads projects that secured four consecutive U.S. National Academy of Medicine Healthy Longevity Catalyst Awards (2021–2024) with Prof. Victor O.K. Li\, advancing AI-driven early diagnosis and drug discovery for Alzheimer’s disease. She co-leads in Co-PI capacity a 50M HKD RGC Theme-based Research Grant for smart air pollution monitoring and health management\, and a 3.25M HKD RGC-SPPR grant in 2011 for cross-border nuclear safety governance\, reflecting her dedication to impactful\, collaborative socially-beneficial research. \nShe cherishes her international collaborations\, including roles as Visiting Senior Research Fellow at the University of Cambridge’s Judge Business School (since 2013)\, Visiting Fellow at Hughes Hall\, and Visiting Academic at the Department of Computer Science and Technology at Cambridge. Prof. Lam a Visiting Scholar at MIT’s Centre for Energy and Environmental Policy Research and MIT EECS in 2019. In collaboration with Prof. Jon Crowcroft\, FRS. At Cambridge\, they have co-organized five AIfSG symposiums since 2018\, fostering global academic dialogue in value-driven AI research. \nIn teaching\, Prof. Lam is committed to mentoring PhD students at HKU\, nurturing innovative thinkers in AIfSG. She co-established the pioneering HKU-Cambridge PhD Pathway\, enabling engineering students to pursue an MPhil in Technology Policy at Cambridge Judge Business School\, and pioneered interdisciplinary courses on Climate Change and Sustainability (2013–2020) and Deep Learning and Applications (2019-2025). As Area Editor of the Cambridge University Press journal Data and Policy\, she contributes to global discussions on value-driven data policy. Her publications span multiple disciplines\, including IEEE Transactions\, Nature Scientific Reports\, Nature Molecular Psychiatry\, Journal of Alzheimer’s Disease\, Environment International\, Applied Energy\, Energy Policy\, and Data and Policy. Co-directing the HKU-AI WiSe and three HKU-Cambridge AI Research Platforms\, Prof. Lam humbly seeks to advance AIfSG. \nAll are welcome!
URL:https://ece.hku.hk/events/20251216-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251215T150000
DTEND;TZID=Asia/Hong_Kong:20251215T160000
DTSTAMP:20260510T151526
CREATED:20251128T013019Z
LAST-MODIFIED:20251212T170833Z
UID:114307-1765810800-1765814400@ece.hku.hk
SUMMARY:Seminar on Large Language Models (LLMs) in Space-Air-Ground Integrated Networks (SAGINs)
DESCRIPTION:*** Please note that the event has been rescheduled to Monday\, December 15\, 2025. *** \nAbstract\nSpace-Air-Ground Integrated Networks (SAGINs)\, an architecture combining satellites\, aerial platforms (like UAVs and High-Altitude Platforms)\, and terrestrial networks\, aim to provide ubiquitous\, high-speed\, and seamless global coverage. Unmanned aerial vehicles (UAVs) have been widely deployed for reliable and energy-efficient data collection from spatially distributed devices\, and hold great promise in supporting diverse Internet of Things (IoT) applications. Recently\, Large Language Models (LLMs) have been used in SAGINs to enable more intelligent\, adaptive\, and autonomous networks\, particularly in the context of 6G and beyond. In this talk\, inspired by the remarkable generalisation and reasoning capabilities of large language models (LLMs)\, an LLM-based channel prediction framework\, namely CPLLM\, to forecast future channel state information (CSI) for LEO satellites based on historical CSI data will be presented. A large language model (LLM)-empowered critic-regularised decision transformer (DT) framework\, termed LLM-CRDT\, to learn effective UAV control policies will also be presented. \nSpeaker\nProf. Arumugam NALLANATHAN\nSchool of Electronic Engineering and Computer Science\,\nQueen Mary University of London \nSpeaker’s Biography\nArumugam NALLANATHAN is Professor of Wireless Communications and the founding head of the   Communication Systems Research (CSR) group in the School of Electronic Engineering and Computer Science at Queen Mary University of London since September 2017. He was with the Department of Informatics at King’s College London from December 2007 to August 2017\, where he was Professor of Wireless Communications from April 2013 to August 2017. He was an Assistant Professor in the Department of Electrical and Computer Engineering\, National University of Singapore from August 2000 to December 2007. His research interests include 6G Wireless Networks and Internet of Things (IoT). He published nearly 900 technical papers in scientific journals and international conferences. His publications have been cited over 36\,000 times with an H-index of 95. He is a co-recipient of number of Best Paper Awards\, including IEEE Communications Society Leonard G. Abraham Prize\, 2022. He has been selected as a Web of Science (ISI) Highly Cited Researcher in 2016\, 2022-2025. He is an IEEE Fellow and IEEE Distinguished Lecturer. \nOrganiser\nProf. Yuanwei LIU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20251218-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:No event,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251214T133000
DTEND;TZID=Asia/Hong_Kong:20251214T180000
DTSTAMP:20260510T151526
CREATED:20251211T093001Z
LAST-MODIFIED:20251211T093001Z
UID:114399-1765719000-1765735200@ece.hku.hk
SUMMARY:ACM SIGGRAPH Asia 2025 Pre-Conference Technical Workshop
DESCRIPTION:Click HERE to view the details.
URL:https://ece.hku.hk/events/20251214-1/
LOCATION:Room CPD-2.42\, 2/F\, The Jockey Club Tower\, Centennial Campus\, HKU
CATEGORIES:Highlights,Seminar
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END:VEVENT
END:VCALENDAR