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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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TZID:Asia/Hong_Kong
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251204T093000
DTEND;TZID=Asia/Hong_Kong:20251204T103000
DTSTAMP:20260511T234700
CREATED:20251125T034514Z
LAST-MODIFIED:20251125T034514Z
UID:114269-1764840600-1764844200@ece.hku.hk
SUMMARY:RPG Seminar – Lightweight Blockchain for Spatially and Temporally Scalable Federated Learning in Edge Networks
DESCRIPTION:Zoom Link:  https://hku.zoom.us/j/97347520963?pwd=ohdjCe9kx6axOTFn2m2M9gsVojb2kG.1 \nAbstract\nFederated Learning (FL) has rapidly advanced as a foundational paradigm for realizing privacy-preserving intelligence in edge networks. However\, its real-world deployment is fundamentally challenged by two dimensions: spatial scalability across a large\, heterogeneous population of devices\, and temporal robustness over long-lived\, evolving learning processes. While blockchain technology offers inherent benefits like tamper-proof logging and decentralized trust\, its naïve integration with FL is often intractable. This intractability stems from complex communication topologies\, severe resource limitations\, and the increasing cost of maintaining and retrieving an ever-expanding\, shared knowledge base. \nThis seminar presents a unified view of lightweight blockchain designs systematically engineered to overcome these challenges. We introduce two novel systems: LiteChain and LiFeChain. LiteChain addresses spatial scalability in massive edge networks. Furthermore\, it incorporates a Comprehensive Byzantine Fault Tolerance (CBFT) consensus and a secure update mechanism to reduce end-to-end latency\, on-chain storage overhead. LiFeChain tackles the temporal dimension in Federated Lifelong Learning (FLL) for edge networks. It is combined with a Segmented Zero-knowledge Arbitration (Seg-ZA) protocol that enables efficient\, bidirectional model verification with minimal on-chain disclosure. Implemented as a plug-and-play component in representative FLL frameworks\, LiFeChain significantly enhances model robustness against long-term\, cumulative attacks while sustaining efficiency and scalability. \nThese works demonstrate a systematic methodology for redesigning blockchain architectures to support FL that is simultaneously capable of scaling out in space and enduring over time within highly constrained edge networks. \nSpeaker\nMiss Handi Chen\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nHandi Chen received the B.E. degree in network engineering from Tianjin University of Since and Technology in 2019\, and the M.E. degree in network engineering from the Dalian University of Technology in 2022. She is currently working toward the Ph.D. degree in Department of Electrical and Electronic Engineering\, the University of Hong Kong. Her research interests include edge intelligence\, mobile edge computing. \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/20251204/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251204T140000
DTEND;TZID=Asia/Hong_Kong:20251204T150000
DTSTAMP:20260511T234700
CREATED:20251201T090429Z
LAST-MODIFIED:20251201T090429Z
UID:114317-1764856800-1764860400@ece.hku.hk
SUMMARY:RPG Seminar – Planning and Operation Optimization of Electric-Coupled Systems for High-Speed Railways towards Flexibility and Resilience
DESCRIPTION:Zoom Link:  https://hku.zoom.us/j/97338207102 \nAbstract\nElectrified high-speed railways are emerging as major and spatially distributed electricity consumers in modern power systems\, and their traction demand is tightly coupled with train dynamics and timetable scheduling. With the increasing exploitation of renewable resource endowments along railway corridors\, electrified railways are evolving from pure loads into potential flexibility and resilience providers for low-carbon power systems. However\, this evolution also brings new challenges and opportunities. On the one hand\, existing models and operation strategies often treat high-speed railways as rigid electrical loads\, leading to simplified kinetic-electrical representations and limited utilization of flexibility arising from coordinated train control and energy management of electric-coupled traction power supply systems. On the other hand\, existing planning approaches for railway energy infrastructure remain largely economy-oriented and seldom incorporate explicit resilience criteria or the multi-stage couplings between energy supply adequacy and transportation service continuity. Hence\, for the first challenge\, we develop a unified kinetic-electrical coupling model together with a space-domain multi-phase pseudospectral coordination framework that jointly optimizes train trajectories and the operation of electric-coupled traction power supply systems with integrated photovoltaics and hybrid energy storage. The proposed method simultaneously accounts for detailed railway operating constraints and power system operating constraints within a numerically efficient optimal control formulation\, demonstrating reduced traction energy cost\, enhanced renewable utilization\, and mitigated power fluctuations at the grid interface compared with benchmark strategies. For the second challenge\, we propose a multi-stage resilience enhancement framework that integrates risk-aware capacity planning\, rolling emergency energy management\, and adaptive train control. A two-stage stochastic program with risk measurements is employed to co-optimize renewable\, storage\, and backup generation capacities under extreme grid outage and adverse weather scenarios\, while operational layers coordinate distributed resources and train trajectories in real time. Case studies show that the proposed framework can substantially improve both energy supply resilience and transportation service robustness with moderate additional cost\, highlighting electrified high-speed railways as promising flexibility and resilience resources in future power systems. \n \nSpeaker\nMr. Ruizhang Yang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nRuizhang Yang received his B.S. and M.S. degree in Electrical Engineering from Huazhong University of Science and Technology\, China in 2017 and 2020\, respectively. From 2020 to 2022\, he worked as an Engineer at the Institute of Electrified Railway Design and Research\, China Railway Siyuan Survey and Design Group. He is currently pursuing a Ph.D. degree at the Department of Electrical and Electronic Engineering at The University of Hong Kong\, under the supervision of Prof. Yunhe Hou. His research interests focus on resilient transportation energy supply systems. \nOrganiser\nProf. Yunhe Hou\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251204-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251205T103000
DTEND;TZID=Asia/Hong_Kong:20251205T113000
DTSTAMP:20260511T234700
CREATED:20251202T021500Z
LAST-MODIFIED:20251202T021500Z
UID:114320-1764930600-1764934200@ece.hku.hk
SUMMARY:RPG Seminar – A Hybrid Iterative Framework for AC Unit Commitment: Integrating Global Linearization Updates with Local Constraint Corrections
DESCRIPTION:Zoom Link:  https://hku.zoom.us/j/96462551842?pwd=MFNEYU1qcmZzeE9Rby9aRVZLQ0RZdz09 \nAbstract\nTo address the computational challenges of AC Unit Commitment (AC-UC)\, this paper proposes a hybrid iterative framework that decomposes the MINLP model into a linearized MILP master problem and an exact AC feasibility check. The approach integrates Taylor-expansion-based linearization with a novel switching strategy that coordinates global updates for initial geometric alignment and local constraint corrections for subsequent stability. By freezing the Jacobian matrix after the initial phase\, the method effectively mitigates integer oscillation. Case studies on IEEE standard test systems verify that the proposed method significantly reduces linearization errors\, improves the quality of unit commitment decisions\, minimizes physical violations and operating costs\, and decreases the number of iterations required for convergence. \nSpeaker\nMiss Miao Cheng\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMiao Cheng received her bachelor’s degree from Beihang University and her master’s degree from Tsinghua University\, both in electrical and electronic engineering. She is currently working toward the Ph.D. degree in electrical and electronic engineering in the Department of Electrical and Electronic Engineering at the University of Hong Kong. Her current research interests include security-constrained unit commitment\, inverter-based resources integration\, non-convex optimization in power system. \nOrganiser\nProf. Yunhe Hou\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251205/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251205T110000
DTEND;TZID=Asia/Hong_Kong:20251205T120000
DTSTAMP:20260511T234700
CREATED:20251118T074008Z
LAST-MODIFIED:20251118T083942Z
UID:113926-1764932400-1764936000@ece.hku.hk
SUMMARY:Seminar on 40 Years of Proton Magnetic Resonance Spectroscopy in Human Brain
DESCRIPTION:Abstract\nThe development of whole-body MRI scanners in the late 1980s at field strengths of 1.5T\, together with other fundamental technological advances such shielded field gradients and single-shot spatial localization techniques\, enabled the non-invasive collection of spectra from the human in just a few minutes of scan time. Since that time\, there have been many technical advances and clinical studies performed\, and it remains an active area of research and development. This presentation will review key technical developments including spatial localization techniques for both single voxel spectroscopy and spectroscopic imaging\, spectral analysis\, spectral editing\, and the effects of increasing magnetic field strength. In addition\, the metabolic information from in vivo MRS will be discussed\, including metabolic changes that can be detected in various pathological states\, and applications in the clinic. Finally\, some of the challenges facing the clinical use of MRS and sustainability will be discussed. \nSpeaker\nProf. Peter BARKER\nDirector of Division of MR Research\nJohn Hopkins University School of Medicine \nSpeaker’s Biography\nPeter BARKER\, D.Phil.\, is a Professor of Radiology and Oncology\, and Director of the Division of MR Research at the Johns Hopkins University School of Medicine in Baltimore\, Maryland. He holds a D.Phil. degree in Physical Chemistry from Oxford University.  Since 1989\, he has been a faculty member of the Russell H. Morgan Department of Radiology and Radiological Science at Johns Hopkins\, where his primary interest has been the development of proton MR spectroscopy\, and other MRI techniques\, for applications in the human brain. He has published over 315 original\, peer-reviewed articles\, more than 45 commentaries\, review articles and book chapters\, as well as 3 books on Clinical MR Neuroimaging\, Spectroscopy and Perfusion Imaging. Dr Barker is a fellow of the ISMRM society\, and an editor for the journals Magnetic Resonance in Medicine and NMR in Biomedicine. \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\n\nAll are welcome!
URL:https://ece.hku.hk/events/20251205-1/
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:20251208T140000
DTEND;TZID=Asia/Hong_Kong:20251208T150000
DTSTAMP:20260511T234700
CREATED:20251121T085327Z
LAST-MODIFIED:20251121T094034Z
UID:114160-1765202400-1765206000@ece.hku.hk
SUMMARY:Seminar on Semiconductor Nanodimer as a Partially Open Terahertz Resonator
DESCRIPTION:The event has been rescheduled to December 8\, 2025 (Monday). \nAbstract\nResonators are often the first apparatus to be constructed and thoroughly investigated when a new region of the spectrum is being explored. From the days of spark-gap generators in early radio transmission to the more recent maser and laser era\, resonant systems have always been essential in enabling a given range of the spectrum to become accessible to electronic communication and instrument applications. With the current interest in terahertz technology\, it would appear logical to search for structures or physical processes that exhibit natural resonances in the terahertz range. Plasma resonance in extrinsic semiconductors can be designed to exhibit field concentration and guiding characteristics that are impetus for sensing and circuitry applications for research and development of terahertz technology. While a single semiconductor nanoparticle (SNP) does exhibit surface plasmon resonance\, the local terahertz field garnered near the two poles of an SNP lacks symmetry and is strongly influenced by the embedding medium. On the other hand\, a semiconductor nanodimer (SND) formed by two SNPs with a gap in between them offers a more secluded environment for field enhancement with better symmetry in field distribution. Considerable attention has been given to metallic nanodimers\, leading to their roles in sensing and antenna applications. On the other hand\, investigations on SNP and SND are currently in the early stage. The salient characteristics of SNDs formed with matched and dissimilar SNPs are discussed in light of their potential for terahertz components and systems development. \nSpeaker\nProf. Thomas WONG\nProfessor Emeritus\,\nDepartment of Electrical and Computer Engineering\,\nIllinois Institute of Technology\nAdjunct Professor of HKU-EEE \nSpeaker’s Biography\nThomas WONG received the B.Sc. degree from the University of Hong Kong\, and the M.S. and Ph.D. degrees from Northwestern University\, all degrees being in Electrical Engineering. He was a Product Engineer at Motorola Semiconductor (HK) before going to the United States for graduate study. He joined Illinois Institute of Technology as a faculty member in 1981 and is currently a Professor Emeritus in the Electrical and Computer Engineering (ECE) Department. He has conducted research in material measurements\, charge transport in ionic and electronic conductors\, transient electromagnetics\, millimeter-wave communication systems\, and propagation effects in high-speed semiconductor devices. In collaboration with Argonne National Laboratory and Fermilab\, he has contributed to research in dielectric loaded accelerators\, coupler design for superconducting multicell cavity resonators\, and nanoscale position sensors. Recent activities have been on space-charge interactions in semiconductor nanostructures. He has served as Graduate Program Director and Department Chair of the ECE Department. In the 1998-1999 academic year he served as the Chair of the University Faculty Council. He is the author of Fundamentals of Distributed Amplification (Artech 1993) and coauthor of Electromagnetic Fields and Waves (Higher Education Press\, 2002 and 2006). He is a Fellow of the International Association of Advanced Materials. \nOrganiser\nIr Dr. King Hang LAM\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20251208-1/
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:20251210T150000
DTEND;TZID=Asia/Hong_Kong:20251210T160000
DTSTAMP:20260511T234700
CREATED:20251202T025642Z
LAST-MODIFIED:20251202T025642Z
UID:114328-1765378800-1765382400@ece.hku.hk
SUMMARY:RPG Seminar – Mamba model acceleration on RRAM-Based Compute-in-Memory (CIM) Systems integrated with Selective State-Space Streaming
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/97793742616?pwd=YIyYlokhzOsap3IvbsbwmfaHVHFoin.1 \nAbstract\nAs Generative AI shifts toward handling massive context windows\, the quadratic complexity of Transformer architecture has become a significant bottleneck. State Space Models (SSMs)\, particularly Mamba\, have emerged as a promising solution\, offering linear-time scaling and superior efficiency. However\, the unique computational duality of SSMs—requiring both memory-intensive projections and agile\, input-dependent state updates—presents new challenges that traditional von Neumann architectures and GPUs struggle to address efficiently. \nThis seminar explores the evolution of efficient sequence modeling and the critical hardware innovations required to support it. We will examine the “Memory Wall” problem in modern AI deployment and introduce Compute-in-Memory (CIM) using Resistive RAM (RRAM) as a paradigm shift to minimize data movement. The discussion will focus on the principles of hardware-software co-design\, illustrating how tailored architecture can bridge the gap between memory-bound operations and dynamic recursions. By integrating specialized streaming dataflows with non-volatile memory technologies\, we can define a new computational fabric capable of enabling the next generation of energy-efficient edge AI. \nSpeaker\nMr. Mingzi Li\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMr. Mingzi Li is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, supervised by Professor Han Wang. He received his B.Eng. in Computer Engineering from The Chinese University of Hong Kong in 2021 and the M.S. in Electrical and Electronic Engineering from The University of Hong Kong in 2022. His research interests include compute-in-memory architectures\, RRAM-based systems\, hardware acceleration for emerging sequence models and efficient AI systems. \nOrganiser\nProf. Han Wang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251210/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251213T140000
DTEND;TZID=Asia/Hong_Kong:20251213T171000
DTSTAMP:20260511T234700
CREATED:20250808T010012Z
LAST-MODIFIED:20251208T021345Z
UID:114346-1765634400-1765645800@ece.hku.hk
SUMMARY:HKU-KAUST Joint Postgraduate Workshop on Computational Imaging 2025
DESCRIPTION:All EEE postgraduate (TPg & RPg) students are welcome! \nThe upcoming “HKU-KAUST Joint Postgraduate Workshop on Computational Imaging 2025” will be held on December 13\, 2025\, organised by the Computational Imaging & Mixed Representation Laboratory. The workshop aims to encourage innovative spirit\, promote excellence and sustain quality\, strive for improvement\, and connect communities. For details of the workshop and speakers\, please visit the event website: https://hku.welight.fun/events/workshop_25Dec \nCoffee\, tea\, and a reception will be provided. \n \nMC\nProf. Evan Y. PENG\, HKU EEE x CS \nCoordinators\nDr. Xin Liu @ HKU; Dr. Qiang Fu @ KAUST \nSpeakers/Guests\n\nWolfgang HEIDRICH\, King Abdullah University of Science and Technology & The University of Hong Kong\nYuhui LIU\, The University of Hong Kong\nNajia SHARMIN\, The University of Hong Kong\nQiang FU\, King Abdullah University of Science and Technology\nErqian DONG\, The University of Hong Kong\nChutian WANG\, The University of Hong Kong\nJiankai XING\, Tsinghua University\nKaixuan WEI\, King Abdullah University of Science and Technology\nZhenyang LI\, The University of Hong Kong\nShi MAO\, King Abdullah University of Science and Technology\nYanmin ZHU\, The University of Hong Kong\nWenbin ZHOU\, The University of Hong Kong
URL:https://ece.hku.hk/events/20251213-1/
LOCATION:Room 602\, Student Commons 6/F\, Pacific Plaza (Off-campus)\, Hong Kong SAR
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251214T133000
DTEND;TZID=Asia/Hong_Kong:20251214T180000
DTSTAMP:20260511T234700
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251215T150000
DTEND;TZID=Asia/Hong_Kong:20251215T160000
DTSTAMP:20260511T234700
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:20251216T141500
DTEND;TZID=Asia/Hong_Kong:20251216T151500
DTSTAMP:20260511T234700
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:20251217T100000
DTEND;TZID=Asia/Hong_Kong:20251217T110000
DTSTAMP:20260511T234700
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/12/1280-5.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251217T110000
DTEND;TZID=Asia/Hong_Kong:20251217T120000
DTSTAMP:20260511T234700
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251219T110000
DTEND;TZID=Asia/Hong_Kong:20251219T120000
DTSTAMP:20260511T234700
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:20251219T150000
DTEND;TZID=Asia/Hong_Kong:20251219T163000
DTSTAMP:20260511T234700
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/12/1280-6.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251223T143000
DTEND;TZID=Asia/Hong_Kong:20251223T153000
DTSTAMP:20260511T234700
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:20251223T143000
DTEND;TZID=Asia/Hong_Kong:20251223T153000
DTSTAMP:20260511T234700
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:20260106T110000
DTEND;TZID=Asia/Hong_Kong:20260106T120000
DTSTAMP:20260511T234700
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/12/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260106T143000
DTEND;TZID=Asia/Hong_Kong:20260106T153000
DTSTAMP:20260511T234700
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260107T140000
DTEND;TZID=Asia/Hong_Kong:20260107T150000
DTSTAMP:20260511T234700
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
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:20260114T140000
DTEND;TZID=Asia/Hong_Kong:20260114T150000
DTSTAMP:20260511T234700
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/12/1280-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260120T100000
DTEND;TZID=Asia/Hong_Kong:20260120T113000
DTSTAMP:20260511T234700
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2026/01/1280-2-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260127T150000
DTEND;TZID=Asia/Hong_Kong:20260127T163000
DTSTAMP:20260511T234700
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:20260129T110000
DTEND;TZID=Asia/Hong_Kong:20260129T120000
DTSTAMP:20260511T234700
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:20260130T100000
DTEND;TZID=Asia/Hong_Kong:20260130T110000
DTSTAMP:20260511T234700
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:20260130T110000
DTEND;TZID=Asia/Hong_Kong:20260130T120000
DTSTAMP:20260511T234700
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260130T153000
DTEND;TZID=Asia/Hong_Kong:20260130T163000
DTSTAMP:20260511T234700
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260206T140000
DTEND;TZID=Asia/Hong_Kong:20260206T150000
DTSTAMP:20260511T234700
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:20260206T150000
DTEND;TZID=Asia/Hong_Kong:20260206T180000
DTSTAMP:20260511T234700
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260210T163000
DTEND;TZID=Asia/Hong_Kong:20260210T180000
DTSTAMP:20260511T234700
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260306T110000
DTEND;TZID=Asia/Hong_Kong:20260306T120000
DTSTAMP:20260511T234700
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
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