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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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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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TZOFFSETFROM:+0800
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260106T110000
DTEND;TZID=Asia/Hong_Kong:20260106T120000
DTSTAMP:20260510T152023
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260106T143000
DTEND;TZID=Asia/Hong_Kong:20260106T153000
DTSTAMP:20260510T152023
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:20260114T140000
DTEND;TZID=Asia/Hong_Kong:20260114T150000
DTSTAMP:20260510T152023
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:20260120T100000
DTEND;TZID=Asia/Hong_Kong:20260120T113000
DTSTAMP:20260510T152023
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:20260127T150000
DTEND;TZID=Asia/Hong_Kong:20260127T163000
DTSTAMP:20260510T152023
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:20260510T152023
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:20260130T153000
DTEND;TZID=Asia/Hong_Kong:20260130T163000
DTSTAMP:20260510T152023
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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