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X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20250101
DTEND;VALUE=DATE:20250301
DTSTAMP:20260511T095859
CREATED:20250213T015036Z
LAST-MODIFIED:20250227T090050Z
UID:109013-1735689600-1740787199@ece.hku.hk
SUMMARY:Call for applications: 2025 TSMC DNA Summer Internship
DESCRIPTION:TSMC\, a world-leading semiconductor foundry\, is recruiting summer interns. Eligible and interested students can find details in the flyer.
URL:https://ece.hku.hk/events/20250117-1/
LOCATION:N/A
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/3434343.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T150000
DTEND;TZID=Asia/Hong_Kong:20241218T160000
DTSTAMP:20260511T095859
CREATED:20241128T022442Z
LAST-MODIFIED:20250211T042315Z
UID:19513-1734534000-1734537600@ece.hku.hk
SUMMARY:Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks
DESCRIPTION:Abstract\nThe evolution of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The proliferation of these applications is underpinned by the mixture of experts (MoE)\, which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE’s efficiencies\, GAI still faces challenges in resource utilization when deployed on local user devices. Therefore\, we first propose mobile edge networks supported MoE-based GAI. Rigorously\, we review the MoE from traditional AI and GAI perspectives\, scrutinizing its structure\, principles\, and applications. Next\, we present a new framework for using MoE for GAI services in Metaverse. Moreover\, we propose a framework that transfers subtasks to devices in mobile edge networks\, aiding GAI model operation on user devices. Moreover\, we introduce a novel approach utilizing MoE\, augmented with Large Language Models (LLMs)\, to analyze user objectives and constraints of optimization problems based on deep reinforcement learning (DRL) effectively. This approach selects specialized DRL experts\, and weights each decision from the participating experts. In this process\, the LLM acts as the gate network to oversee the expert models\, facilitating a collective of experts to tackle a wide range of new tasks. Furthermore\, it can also leverage LLM’s advanced reasoning capabilities to manage the output of experts for joint decisions. Lastly\, we insightfully identify research opportunities of MoE and mobile edge networks. \nSpeaker\nProf. Dusit Niyato\nPresident’s Chair Professor\,\nCollege of Computing & Data Science (CCDS)\,\nNanyang Technological University\, Singapore \nBiography of the Speaker\nDusit Niyato is a President’s Chair Professor in the College of Computing & Data Science (CCDS)\, Nanyang Technological University\, Singapore. Dusit’s research interests are in the areas of mobile generative AI\, edge intelligence\, quantum computing and networking\, and incentive mechanism design. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award. Currently\, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 34.4 for 2023) and will serve as the Editor-in-Chief of IEEE Transactions on Network Science and Engineering (TNSE) from 2025. He is also an area editor of IEEE Transactions on Vehicular Technology (TVT)\, topical editor of IEEE Internet of Things Journal (IoTJ)\, lead series editor of IEEE Communications Magazine\, and associate editor of IEEE Transactions on Wireless Communications (TWC)\, IEEE Transactions on Mobile Computing (TMC)\, IEEE Wireless Communications\, IEEE Network\, IEEE Transactions on Information Forensics and Security (TIFS)\, IEEE Transactions on Cognitive Communications and Networking (TCCN)\, IEEE Transactions on Services Computing (TSC)\, and ACM Computing Surveys. Dusit is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024-2026. He was named the 2017-2023 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET. \nOrganiser\nProf. Hongyang Du\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241218-1/
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/2024/11/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T103000
DTEND;TZID=Asia/Hong_Kong:20241218T113000
DTSTAMP:20260511T095859
CREATED:20241210T012223Z
LAST-MODIFIED:20250211T042315Z
UID:107413-1734517800-1734521400@ece.hku.hk
SUMMARY:Unlocking the Value of Single Modality through Multi-Modal Knowledge Transfer for Healthcare
DESCRIPTION:Abstract\nRecent years have witnessed the remarkable success of deep neural networks in healthcare\, particularly in the analysis of medical images and signals. However\, their performance is often constrained by the scarcity of labelled data\, driven by high labelling costs and challenges related to data privacy and sharing. In this talk\, I will explore how we can overcome these limitations by leveraging multi-modal data through advanced learning frameworks to enhance the capabilities of single modality analysis. Specifically\, I will present our recent and ongoing work\, including those accepted at MICCAI 2023 and MICCAI 2024. This talk will delve into the details of innovative techniques such as large language model-informed pretraining and multi-modal learning for X-ray images and ECG signals\, as well as demonstrating how these approaches can significantly contribute to more accurate\, reliable\, and cost-effective healthcare solutions. \nSpeaker\nProf. Chen (Cherise) Chen\nLecturer (Assistant Professor) in Computer Vision\,\nSchool of Computer Science\,\nUniversity of Sheffield\, UK \nBiography of the Speaker\nChen (Cherise) Chen is currently a Lecturer (assistant professor) in Computer Vision at the School of Computer Science\, University of Sheffield\, UK. Previously\, she was a postdoc at Imperial College London (ICL) and then the University of Oxford. She obtained her MSc and Ph.D. from the Department of Computing at Imperial College London in 2016 and 2022\, respectively\, where she worked closely with Prof. Daniel Rueckert and Dr. Wenjia Bai. Chen also has accumulated valuable industrial experience. She worked as a research scientist at Infervision Inc. in Beijing in 2017\, prior to her PhD\, and later as a part-time research scientist at HeartFlow\, UK\, in 2022 following her PhD. Her research focuses on the intersection of AI and healthcare\, particularly in developing data-efficient\, robust\, and explainable AI for clinical applications. So far\, she has published more than 40 papers in leading conferences and high-impact journals on deep learning for medical data analysis such as MICCAI\, ECCV\, IEEE TMI\, and Medical Image Analysis\, accumulating over 2\,000 Google Scholar citations and an h-index of 20. She is a program chair for MIDL 2025; session and area chair for MICCAI 2024 and serves as lead organisers in several MICCAI workshops and challenges including\, MICCAI ADSMI 2024\, DALI 2023\, and the CMRxMotion Challenge. Very recently\, she has also been appointed as an ELLIS Scholar at the European Laboratory for Learning and Intelligent Systems in 2024. https://cherise215.github.io. \nOrganiser\nProf. Cheng Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241218-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/12/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241209T140000
DTEND;TZID=Asia/Hong_Kong:20241209T150000
DTSTAMP:20260511T095859
CREATED:20241205T041102Z
LAST-MODIFIED:20250211T042315Z
UID:19528-1733752800-1733756400@ece.hku.hk
SUMMARY:Advancing Accessible Ultra-low-field MRI with Deep Learning
DESCRIPTION:Abstract\nMagnetic Resonance Imaging (MRI) is a versatile medical imaging modality. Despite its crucial role in modern healthcare\, MRI remains largely inaccessible to the general population. The development of ultra-low-field (ULF) MRI offers opportunities for enabling low-cost\, low-power\, and potentially portable clinical applications. However\, the imaging performance of these emerging ULF MRI scanners remains poor due to the significantly lower signal-to-noise ratio resulting from the weaker magnetic field. Recent advancements in deep learning have opened new frontiers to tackle this unique challenge. This talk will introduce a paradigm shift in improving ULF MR image quality and reconstructing MR image through supervised deep learning techniques. Leveraging existing high-quality high-field MRI data for training\, DL models can recover structural details buried in noise\, artefacts and poor resolution of raw ULF MR images. Such approaches have the potential to overcome the limitations of poor image quality and pave the way for clinical adoption of ULF MRI. \nSpeaker\nDr. Vick Lau\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nDr. Vick Lau obtained his MEng degree in Biomedical Engineering from Imperial College London in 2019. He recently received his PhD degree in Electrical and Electronic Engineering from the University of Hong Kong in 2024. His research focuses on the application of deep learning techniques to MRI image processing\, restoration\, reconstruction and analysis\, particularly for ULF and accessible MRI. \nOrganiser\nProf. Ed X. Wu\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241209-1/
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/2024/12/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241205T150000
DTEND;TZID=Asia/Hong_Kong:20241205T160000
DTSTAMP:20260511T095859
CREATED:20241120T071853Z
LAST-MODIFIED:20250211T042315Z
UID:19462-1733410800-1733414400@ece.hku.hk
SUMMARY:Multimode Fiber Communication and Multicore Fiber Endoscopy Exploiting Physics-informed Deep Neural Networks
DESCRIPTION:Abstract\nAdvances in fiber-optic systems are crucial for the internet. To cope with this continued exponential growth as Keck´s law\, multimode fibers (MMF) with spatial division multiplexing (SDM) are proposed. However\, there are challenging mode scattering effects in MMF. Physics-informed deep learning enables to correct the scattering\, resulting in advancing security and data rate. We highlight also 3D imaging with lensless multicore fiber endoscopes exploiting learnable multiple Wiener net. Multimode fiber communication and multicore fiber endoscopy are promising for advancements of the internet of things and of biomedical diagnostics and therapy. \nSpeaker\nProf. Jürgen Czarske\nDirector and Full Chair Professor，\nTU Dresden\, Germany \nBiography of the Speaker\nJuergen W Czarske (Fellow EOS\, OPTICA\, SPIE\, IET\, IOP) is director and full chair professor of the TU Dresden\, Germany. His awards include the 2019 OPTICA Joseph-Fraunhofer-Award/Robert-M.-Burley-Prize in Optical Engineering and the 2024 SPIE Dennis Gabor Award in Diffractive Optics. Juergen fosters talented students early. The students and members of his lab have won over 100 prizes\, including Bertha-Benz award of Daimler Benz Foundation (10\,000 Euro). Juergen is Vice President of International Commission for Optics\, ICO\, and was the general chair of the world congress ICO-25\, which was co-sponsored by OPTICA\, SPIE\, IEEE\, Zeiss\, DGaO-The German Branch of EOS\, IUPAP. 3 Nobel laureates have delivered plenary lectures and the participants came from 5A (America\, Asia\, Australia\, Africa and Amazing Europe). \nOrganiser\nProf. Kevin K.M. Tsia\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nSupported by\nInnovation Wing Two \nAll are welcome!
URL:https://ece.hku.hk/events/20241205-1/
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/2025/01/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241205T110000
DTEND;TZID=Asia/Hong_Kong:20241205T120000
DTSTAMP:20260511T095859
CREATED:20241126T020836Z
LAST-MODIFIED:20250211T042315Z
UID:19487-1733396400-1733400000@ece.hku.hk
SUMMARY:Empowering High-Performance Computing Through Heterogeneous Integration of Power Electronics
DESCRIPTION:Abstract\nMost power electronic equipment\, including power supplies for Artificial Intelligence (AI) and high-performance computing systems\, has traditionally been designed using discrete active and passive components. However\, the power electronics industry has reached a stage where improving one performance attribute often comes at the expense of others. As AI and high-performance computing systems demand increasingly higher power levels\, existing power delivery architectures are proving inadequate. Delivering the required power to server racks\, circuit boards\, accelerator cards\, and AI processors has become an escalating challenge. The emergence of wide-bandgap (WBG) power semiconductor devices\, such as silicon carbide (SiC) and gallium nitride (GaN)\, presents a breakthrough opportunity. Compared to silicon (Si) devices\, WBG technologies offer significantly lower losses\, providing a promising solution to the energy challenges of high-performance computing. However\, current design practices often adopt a ‘plug-and-play’ approach\, merely replacing Si with WBG components without altering the underlying design principles. This results in only incremental improvements in efficiency and power density\, leaving the transformative potential of WBG devices untapped. This presentation will explore a paradigm shift in powering AI and high-performance computing systems through the use of high-frequency heterogeneous integration in WBG power electronics design. This innovative approach delivers simultaneous advancements in all critical performance metrics\, including efficiency\, power density\, and electromagnetic compatibility (EMC). Furthermore\, this approach can streamline traditionally labor-intensive manufacturing processes\, paving the way for significant advancements in overall production efficiency. \nSpeaker\nProf. Qiang Li\nFull Professor\,\nCenter for Power Electronics Systems (CPES)\,\nVirginia Tech \nBiography of the Speaker\nQiang Li received the B.S. and M.S. degrees from Zhejiang University\, China\, in 2003 and 2006\, respectively\, and the Ph.D. degree from Virginia Tech\, Blacksburg\, VA\, in 2011. He is currently a Full Professor in the Center for Power Electronics Systems (CPES) at Virginia Tech. His research interests include high-frequency power conversion and control\, high-density electronics packaging and magnetic integration\, as well as power solutions for high-performance computing\, data centers\, electric vehicles\, and energy storage systems. With over 300 peer-reviewed technical publications\, including 100 journal articles\, he has received eight prize paper awards and holds 26 U.S. patents. He currently serves as the Chair of Academic Affairs for the IEEE Power Electronics Society and is an associate editor for both the IEEE Transactions on Power Electronics and the IEEE Journal of Emerging and Selected Topics in Power Electronics. Dr. Li is also a recipient of the U.S. National Science Foundation (NSF) Career Award. \nOrganiser\nProf. Han Wang \nAll are welcome!
URL:https://ece.hku.hk/events/20241205-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/1280-copy.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241204T100000
DTEND;TZID=Asia/Hong_Kong:20241204T113000
DTSTAMP:20260511T095859
CREATED:20241127T084902Z
LAST-MODIFIED:20250114T025206Z
UID:19511-1733306400-1733311800@ece.hku.hk
SUMMARY:EPFL’s Centers Blueprint to Revolutionize Sustainable AI Co-Design and Optimization
DESCRIPTION:Abstract\nArtificial Intelligence (AI) is transforming our world by including self-learning capabilities or extracting intriguing hidden patterns from input data. While the cloud-based computing paradigm has been a baseline approach for AI inferences in recent years\, technological advances and AI optimization methods advocate a shift toward an edge-computing alternative. Nevertheless\, combining cloud computing with the new edge AI paradigm poses storage\, computational\, and efficient communication challenges that must be addressed to support the deployment of compute-intense algorithms in embedded devices. In particular\, with the continuous increase in the quality of their outputs\, large AI models trained in the cloud have high memory and computing requirements that strain our natural resources worldwide and limit their porting in edge nodes. \nAware of this challenge\, the Swiss Federal Institute of Technology Lausanne (EPFL) is studying the problem from different perspectives by combining the works of its different Research Centers. Accordingly\, this presentation will first cover how a multi-center focused approach (in close collaboration with key industrial players) was used to conceive a new energy-efficient AI supercomputer and sustainable data center to explore the cooperation of cloud and edge AI systems. Second\, this presentation will cover a new co-design strategy for new edge AI systems that interact with the latest sustainable data center designs. This new co-design strategy advocates hardware-aware algorithmic transformations of large AI systems to enable accuracy-driven embedded ensembles of convolutional Neural Networks (ECNNs) to improve the accuracy and robustness of final edge devices. Third\, this presentation will discuss the possible use of codebook-based representations\, approximate computing\, and in-memory computing accelerators to reduce further the energy consumption of the next-generation edge AI systems. \nSpeaker\nProf. David Atienza\nEmbedded Systems Laboratory (ESL)\,\nEcole Polytechnique Federale de Lausanne (EPFL)\,\nLausanne\, Switzerland \nBiography of the Speaker\nDavid Atienza is a Professor of Electrical and Computer Engineering\, Heads the Embedded Systems Laboratory (ESL)\, and is the Associate Vice President of Research Centers and Plaforms for the period 2024-2028 at Ecole Polytechnique Federale de Lausanne (EPFL)\, Switzerland. His research interests include system-level design methodologies for multi-processor system-on-chip (MPSoC) targeting low-power Cyber-Physical Systems (CPS) and energy-efficient computing servers. His latest works include new 2.5D/3D power/thermal-aware design and architectures for MPSoCs targeting edge AI systems\, as well as HW/SW co-design and AI-based multi-level optimization for sustainable computing in the Internet of Things (IoT) context. \nProf. David Atienza has co-authored over 450 papers\, one book\, and 14 patents in these previous areas. He has also received multiple recognitions and awards\, among them the IEEE/ACM HW/SW Co-Design Conference (CODES-ISSS) 2024 Test-of-Time Award for the most influential paper in the last 15 years\, the ICCAD 10-Year Retrospective Most Influential Paper Award in 2020\, the Design Automation Conference (DAC) Under-40 Innovators Award in 2018\, and IEEE CEDA and ACM SIGDA Early Career Awards on EDA tools and systems research. He is a Fellow of IEEE\, Fellow of ACM\, and has been the Chair of the European Design Automation Association (EDAA) since 2022 until 2024. He is currently the Editor-in-Chief of IEEE Trans. on CAD (TCAD) and ACM Computing Surveys. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nCo-organiser\nProf. Xianhao Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241204-1/
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/2025/01/1280-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241202T140000
DTEND;TZID=Asia/Hong_Kong:20241202T150000
DTSTAMP:20260511T095859
CREATED:20241119T074449Z
LAST-MODIFIED:20250114T025342Z
UID:19456-1733148000-1733151600@ece.hku.hk
SUMMARY:EEE MasterClass (EEE 大師講堂) - Controls in Power Systems with High Renewable Penetration
DESCRIPTION:Abstract\nThis presentation will discuss converter control when power systems are operating with high (over 50%) inverter-based renewable generation.  Traditional synchronous generator controls are set to handle transient\, voltage\, damping\, and frequency stability issues arising from contingencies.  With the anticipated retirement of many steam units and increased replacement by renewable resources and storage systems\, the total system inertia and synchronizing torque will be decreased\, affecting power system stability properties and increasing the likelihood of cascading blackouts.  The focus of this talk is to discuss various issues and approaches to enable a smooth transition from fully synchronous generation to a high percentage of renewable resources with inverter interfaces. The topics covered include frequency and voltage regulation\, grid-forming and grid-following converters\, inertia estimation\, transient stability enhancement\, and oscillation detection. The presentation is aimed at addressing an audience with an interest in energy systems and feedback controls. \nSpeaker\nProf. Joe H. Chow\nElectrical\, Computer\, and Systems Engineering\,\nRensselaer Polytechnic Institute \nBiography of the Speaker\nJoe Chow is Institute Professor Emeritus and Senior Research Scientist\, Electrical\, Computer\, and Systems Engineering at Rensselaer Polytechnic Institute\, Troy\, New York. He received his MS and PhD degrees from the University of Illinois\, Urbana-Champaign. He is a fellow of IEEE and a member of the US National Academy of Engineering. His research interests include power system dynamics and control\, synchronized phasor measurements\, and integration of renewable resources.  He has received several awards\, including the Donald Eckman Award from the American Automatic Control Council\, the Control Technology Award from the IEEE Control Systems Society\, the Charles Concordia Power Engineering Award from the IEEE Power and Energy Society\, and the IEEE Herman Halperin Electric Transmission and Distribution Award. He is the author and co-author of several books\, including a recent Wiley textbook\, entitled “Power System Modeling\, Computation\, and Control” and the upcoming book “Power System Oscillations – second edition.” \nOrganiser\nProf. Y. Hou\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241202-1/
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/2024/11/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241202T103000
DTEND;TZID=Asia/Hong_Kong:20241202T113000
DTSTAMP:20260511T095859
CREATED:20241125T064139Z
LAST-MODIFIED:20250114T030110Z
UID:19470-1733135400-1733139000@ece.hku.hk
SUMMARY:An Overview of Fluid Antenna Systems
DESCRIPTION:Abstract\n“Be formless … shapeless\, like water!”\, which were the words used by Bruce Lee\, as he was revealing the philosophy of Jeet Kune Do\, the martial arts system Lee founded in 1967. Many parallels can be drawn in wireless communications technologies where engineers have been seeking greater flexibility in using the spectral and energy resources for improving network performance. In this talk\, I will speak on the emerging concept of fluid antenna system (FAS) which represents position-flexible shape-flexible antenna technologies for wireless communications. I will cover some basics of FAS and briefly review the latest results. \nSpeaker\nProf. Kai-Kit WONG\nChair Professor of Wireless Communications\,\nDepartment of Electronic and Electrical Engineering\,\nUniversity College London \nBiography of the Speaker\nProf. Kai-Kit Wong received the BEng\, the MPhil\, and the PhD degrees\, all in Electrical and Electronic Engineering\, from the Hong Kong University of Science and Technology\, Hong Kong\, in 1996\, 1998\, and 2001\, respectively. He is Chair Professor of Wireless Communications at the Department of Electronic and Electrical Engineering\, University College London. His current research centers around 6G mobile communications. He is one of the early researchers who proposed multiuser MIMO. His first paper on multiuser MIMO was published in WCNC 2000 which appeared to be the first ever research paper on this topic. He is Fellow of IEEE and IET. He served as the Editor-in-Chief for IEEE Wireless Communications Letters between 2020 and 2023. \nOrganizer\nProf. Yuanwei Liu \nAll are welcome!
URL:https://ece.hku.hk/events/20241202-3/
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/2024/11/1280-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241202T090000
DTEND;TZID=Asia/Hong_Kong:20241202T100000
DTSTAMP:20260511T095859
CREATED:20241127T040706Z
LAST-MODIFIED:20250114T030152Z
UID:19508-1733130000-1733133600@ece.hku.hk
SUMMARY:Multi-Objective Management for Many-Core Computing Servers
DESCRIPTION:Abstract\nIn recent decades\, the rapid growth of cloud computing has posed significant challenges in managing the reliability\, performance\, and energy efficiency of complex multi-core computing servers. This talk introduces strategies to address these challenges using multi-objective optimization methods that span MultiProcessor System On Chip (MPSoC)\, server\, and application levels. Topics include the development of an accelerated thermal simulation framework at the MPSoC level\, advanced reliability management schemes in the context of high-performance computing servers\, and energy-efficient\, workload-aware frequency scaling techniques at the application level. Together\, we will explore ways to create more sustainable and efficient cloud computing systems. \nSpeaker\nDr. Darong Huang\nÉcole Polytechnique Fédérale de Lausanne (EPFL) \nBiography of the Speaker\nDarong Huang received his B.Sc. and M.Sc. degrees in Electrical Engineering from the University of Electronic Science and Technology of China (UESTC) in 2016 and 2019\, respectively. He obtained his Ph.D. degree from École Polytechnique Fédérale de Lausanne (EPFL) in 2024 and will join as a postdoctoral researcher in 2025. His research focuses on thermal and reliability management for MultiProcessor System On Chip (MPSoC); multi-objective management and optimization of cloud servers. Moving forward\, he aims to explore hardware and software co-design and optimization for both cloud and edge devices\, striving for holistic global optimization. \nAll are welcome!
URL:https://ece.hku.hk/events/20241202-2/
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/2024/11/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241129T150000
DTEND;TZID=Asia/Hong_Kong:20241129T160000
DTSTAMP:20260511T095859
CREATED:20241125T025933Z
LAST-MODIFIED:20250114T031215Z
UID:19465-1732892400-1732896000@ece.hku.hk
SUMMARY:RPG Seminar – Discrimination of Developmental Trajectories in Brain Structural Features of ASD
DESCRIPTION:Zoom ID: 998 6183 8733\nPassword: 275656 \nAbstract\nAutism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in social interaction\, communication\, and repetitive behaviors. Structural brain abnormalities associated with ASD are known to evolve across the developmental trajectory\, making it difficult to identify consistent biomarkers for accurate individual diagnosis. In this seminar\, we explore age-related variations in cortical thickness (CT) and gray matter volume (GMV) using a novel Partial Least Squares (PLS)-based method combined with feature selection techniques. Key findings reveal significant developmental abnormalities in several brain regions\, which were strongly correlated with behavioral measures such as Autism Diagnostic Observation Schedule (ADOS) scores. The incorporation of age-related features not only enhanced classification accuracy but also provided new insights into the developmental and behavioral mechanisms underlying ASD. These results highlight the critical role of age in understanding ASD and improving diagnostic and therapeutic strategies. \nSpeaker\nMs. Guo Zifan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nZifan Guo received the B.S. degree from the University of Electronic Science and Technology of China\, Chengdu\, China\, in 2021. She is pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong. \nOrganizer\nProf. S. C. Chan \nAll are welcome.
URL:https://ece.hku.hk/events/20241129-2/
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:20241129T100000
DTEND;TZID=Asia/Hong_Kong:20241129T110000
DTSTAMP:20260511T095859
CREATED:20241125T025653Z
LAST-MODIFIED:20250114T031324Z
UID:19464-1732874400-1732878000@ece.hku.hk
SUMMARY:RPG Seminar – Wireless Sensing for Speech Recovery and Recognition
DESCRIPTION:Abstract\nConsidering the microphone is easily affected by noise and soundproof materials\, the radio frequency (RF) signal is a promising candidate to recover audio as it is immune to noise and can traverse many soundproof objects. We introduce Radio2Speech\, a system that uses RF signals to recover high quality speech from the loudspeaker. Radio2Speech can recover speech comparable to the quality of the microphone\, advancing from recovering only single tone music or incomprehensible speech in existing approaches. Quantitative and qualitative evaluations show that in quiet\, noisy and soundproof scenarios\, Radio2Speech achieves state-of-the-art performance and is on par with the microphone that works in quiet scenarios. Moreover\, millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications\, such as conference speech transcription and eavesdropping. However\, considering the practicality in real scenarios\, latency and recognizable vocabulary size are two critical factors that cannot be overlooked. We also propose Radio2Text\, the first mmWave-based system for streaming automatic speech recognition (ASR) with a vocabulary size exceeding 13\,000 words. The experimental results show that our Radio2Text can achieve a character error rate of 5.7% and a word error rate of 9.4% for the recognition of a vocabulary consisting of over 13\,000 words. \nSpeaker\nMr. Zhao Running\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nRunning Zhao is pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, and he is advised by Edith C.H. Ngai. He received the B.S. and M.S. degree from Wuhan University of Technology\, Wuhan\, China\, in 2018 and 2021. His current research interests include human-computer interaction\, ubiquitous computing\, and multimodal learning\, with a particular emphasis on using different modalities or sensors to investigate applications for HCI and healthcare. \nOrganizer\nProf. Edith C.H. Ngai \nAll are welcome.
URL:https://ece.hku.hk/events/20241129-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241128T153000
DTEND;TZID=Asia/Hong_Kong:20241128T170000
DTSTAMP:20260511T095859
CREATED:20241112T041820Z
LAST-MODIFIED:20250220T080029Z
UID:19431-1732807800-1732813200@ece.hku.hk
SUMMARY:Navigating Your Dream Career in Finance and Technology
DESCRIPTION:The Department has extended an invitation to an entrepreneur and researcher\, Dr. David Ng\, together with three experienced professionals\, Mr. David Sun Tak-kei\, Ms. Yan Yu\, and Mr. Terry Hao to share their valuable experience with you. By attending\, you will have the chance to broaden your perspective on career options. \n \nDate: 28 November\, 2024\nTime: 15:30 – 17:00\nVenue: Room 603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong\nSpeakers: Dr. David Ng\, Mr. David Sun Tak-kei\, and Ms. Yan Yu \nBiography of the speakers: \n\nDr. David Ng\nPhD\, EMBA\, MSc\, BEng\, MHKIE\, MIEEE\nAdjunct Assistant Professor\, HKU-EEE \nCEO & CTO\, High Tech Technology (https://www.hightt.com/)\nCEO & CTO\, FinSiliconX (https://www.fsx100.com/) \nDr. Ng received his PhD degree in Electrical Engineering from HKU\, where his engineering talents were duly honed. Dr. Ng is a current member of HKIE and IEEE with more than 20 patents to his name from US and China over the years. Dr. Ng graduated with an electronic engineering degree from the HKUST. He subsequently completed an MSc degree in Electronic & Information Engineering\, and Executive MBA (EMBA) degree from the CityU\, and had worked in Motorola Semiconductor\, Arizona\, USA. \nAs an entrepreneur and architectural IC design engineer with over 20 years’ substantial work experiences in high-tech industry\, Dr. Ng has founded several local technology companies focused on IC design\, loT-smart home\, Al algorithm for big data mining\, and digital control under funding by multinational companies and venture capitals after his respective service with ASTRI (Hong Kong Government Agency)\, ON Semiconductor (a NASDAQ-Iisted semiconductors company) and Motorola (an NYSE-listed Telecom Company). Dr. Ng possesses a track record of raising funds amounting to millions of US dollars from the HKSAR Government and industries to sponsor next-generation IC design technologies. \nDuring his years in ASTRI\, Dr. Ng successfully helped a corporate client in getting listed in the GEM Board of the HKEX by licensing the IP of Dr. Ng’s team for production. \nWith special interests in both fields of green power management and sensors\, Dr. Ng’s research areas range from efficient & safe LED driver ICs\, digital-protocol fast-battery-charging ICs\, energy-harvesting enabling circuits\, to low- impedance bandgap reference.\nAs far as sensors are concerned\, Dr. Ng specializes in low-noise amplifiers\, analog-to-digital\, digital-to-analog data converters\, µV (10 -6 V) bio-signal detection of brain or heart signals (EEG/ECG) and digital signal processing (DSP) like Fast Fourier Transform (FFT). \nOver the years\, Dr. Ng has gained solid experiences in building from scratch R&D teams of 10 to 50 engineers with fast revenue returns while commercializing product ICs for mass production (over 16 so far\, with some attaining Automotive Grade). \nDr. Ng has also completed CityU’s EMBA degree\, with special focuses on VC\, PE and merger & acquisitions (M&A) in tech companies. Being listed in “Beta Gamma Sigma”\, he keeps on sharpening his professional business development skills. \nDr. Ng frequently speaks in seminars on IC designs\, brain sensors\, power electronics\, fast battery-charging and LED driver ICs. He has published over ten academic papers. An adjunct assistant professor and honorary lecturer in HKU and other tertiary institutions\, Dr Ng has taught a variety of engineering courses including master-level ones and supervised postgraduate students for their dissertations for 16 years. \n\nMr. David Sun GBS JP \nDavid Sun Tak-kei\, GBS\, JP was the Director of Audit of Hong Kong\, a “principal official” position\, between 2012 and 2018\, and was the President of the Hong Kong Institute of Certified Public Accountants between 2003 and 2007 (Hong Kong Society of Accountants before 8 September 2004). \nSun began his career at Ernst & Young in 1977 and was the Far East Co-Area Managing Partner\, until his retirement in 2010. He was the partner in charge of the Akai Holdings account from 1991 to 1999. \nSun was a member of Securities and Futures Commission between 2001 and 2007. In 2003\, he became the president of the Hong Kong Institute of Certified Public Accountants (HKICPA) and served until 2007.Sun was later appointed as Director of Audit of Hong Kong in July 2012. He retired in December 2018. \n\nMiss Yan Yu \nYan Yu was born in China and moved to the UK in her teens. After obtaining an MSc from Warwick University (with Distinction) she joined Morgan Stanley as an analyst and spent 14 years on Wall Street as a derivatives trader\, working for banks like Goldmans Sachs and Merrill Lynch in London\, New York and Moscow before relocating to Hong Kong in 2010. \nIn 2018 Yan left her career on Wall Street to pursue seminary training at the Lutheran Theological Seminary\, obtaining an MA (in Theology) and an MTh (in Old Testament) by 2020\, before being invited to teach Biblical Hebrew and Old Testament as a visiting lecturer there. Yan is currently serving as a Church Minister. \nYan is also a serial entrepreneur and co-founder of a Medical AI company based in Cyberport. Yan recently started her PhD studies with HKU Med\, her research topic is Christian cancer care.
URL:https://ece.hku.hk/events/20241128-4/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Career Talks,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241128T140000
DTEND;TZID=Asia/Hong_Kong:20241128T150000
DTSTAMP:20260511T095859
CREATED:20241120T015524Z
LAST-MODIFIED:20250114T031758Z
UID:19459-1732802400-1732806000@ece.hku.hk
SUMMARY:The Future of AI in Vision and Healthcare: Multimodal Large Language Models for Image Perception\, Restoration\, and Medical Applications
DESCRIPTION:Abstract\nThe rapid advancement of artificial intelligence\, driven by the emergence of multimodal large language models (MLLMs)\, is transforming the fields of computer vision and healthcare. By integrating diverse modalities\, these models enable groundbreaking progress in image perception\, restoration\, and medical applications. This talk explores how MLLMs leverage foundational model paradigms—incorporating billions of parameters and vast\, heterogeneous datasets—to unify vision-centric tasks within a natural language framework. Key innovations include open-world image understanding\, unified vision-language models\, MLLM-augmented image restoration\, and generalized medical diagnosis. By aligning vision tasks with language instructions\, these models overcome traditional constraints\, enabling user-defined operations\, advanced visual reasoning\, and complex diagnostic capabilities. This talk also explores how MLLMs are transforming the landscape of vision and healthcare\, establishing a robust foundation for the next generation of AI systems. \nSpeaker\nDr. Xiaowei Hu\nResearch Scientist\,\nShanghai Artificial Intelligence Laboratory \nBiography of the Speaker\nDr. Xiaowei Hu is a Research Scientist at the Shanghai Artificial Intelligence Laboratory\, with expertise spanning computer vision\, low-level vision\, vision perception\, medical AI\, and deep learning. He earned his Ph.D. in Computer Science and Engineering from the Chinese University of Hong Kong and has authored over 50 academic papers in top journals and conferences in computer vision\, including IEEE TPAMI\, CVPR\, and ICCV. Dr. Hu has earned recognition as one of the World’s Top 2% Scientists by Stanford University for 2022-2024 and is also a recipient of China’s Outstanding Young Talents Program (Overseas). \nOrganiser\nProf. Cheng Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241128-5/
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:20241128T113000
DTEND;TZID=Asia/Hong_Kong:20241128T123000
DTSTAMP:20260511T095859
CREATED:20241108T035057Z
LAST-MODIFIED:20250114T031829Z
UID:19428-1732793400-1732797000@ece.hku.hk
SUMMARY:RPG Seminar – Multi-time-scale management of virtual power plant for frequency regulation service provisio
DESCRIPTION:Abstract\nRenewable integration in power systems causes a surge of frequency fluctuations\, primarily due to the intermittent nature of renewable energy. To address this challenge\, distributed energy resources (DERs) pave one promising way for frequency regulation by their aggregation as a virtual power plant (VPP). However\, the technical heterogeneity of DERs results in varying frequency regulation performance across different DER portfolios. To promote the performance-based profit for frequency regulation\, we propose a data-driven approach to model the frequency regulation performance with the DER portfolio\, which is integrated into a VPP’s operation schemes. On this basis\, the optimal operations of the VPP in different time scales are explored for frequency regulation service provision\, including both hour-ahead frequency reserve bidding and real-time reserve activation. A comprehensive technical and economic analysis is conducted to validate the effectiveness of the proposed approach in enhancing VPP’s profit for frequency regulation service provision. \nSpeaker\nMiss Huang Mingyu\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nMingyu Huang received the B.S. degree in automation and the M.S. degree in control science and engineering from Wuhan University\, Wuhan\, China\, in 2019 and 2022\, respectively. She is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong. Her research interests include dynamic virtual power plants and ancillary services. \nOrganizer\nProf. Yi Wang \nAll are welcome.
URL:https://ece.hku.hk/events/20241128-1/
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:20241128T103000
DTEND;TZID=Asia/Hong_Kong:20241128T113000
DTSTAMP:20260511T095859
CREATED:20241108T035452Z
LAST-MODIFIED:20250114T031919Z
UID:19430-1732789800-1732793400@ece.hku.hk
SUMMARY:RPG Seminar – Learning-Assisted Optimal Power Flow for Power Systems: Addressing Feasibility\, Adaptability\, and Scalability Issues
DESCRIPTION:Abstract\nOptimal Power Flow (OPF) problem has been studied for many years. Traditionally\, this problem is solved using an iterative algorithm like the Interior Point Method (IPM). However\, due to the non-convex and nonlinearity of the OPF problem\, it is challenging for iterative algorithm to meet the real-time requirement of power system operation and security analysis. Thanks to the powerful fitting capabilities of the machine learning models and the fact that they can provide a solution in real time\, leveraging such models to solve the OPF problems has become a hot topic. Even though a large number of papers are published to expedite the solving process\, three primary issues are hindering their application. In this seminar\, we would like to address feasibility\, adaptability\, and scalability issues of the existing data-driven OPF methods by employing learning and optimization. Additionally\, we will also discuss open issues in machine learning for solving OPF problem. \nSpeaker\nMr. Jia Yixiong\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nYixiong Jia received the B.S. degree in science from Northwest Agriculture and Forestry University and the M.S. degree in electronic science and technology from ShanghaiTech University\, Shanghai\, China\, in 2019 and 2022\, respectively. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His research interests include data-driven optimal power flow and power flow linearization in power systems. \nOrganizer\nProf. Yi Wang \nAll are welcome!
URL:https://ece.hku.hk/events/20241128-3/
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:20241128T093000
DTEND;TZID=Asia/Hong_Kong:20241128T103000
DTSTAMP:20260511T095859
CREATED:20241108T035312Z
LAST-MODIFIED:20250114T032000Z
UID:19429-1732786200-1732789800@ece.hku.hk
SUMMARY:RPG Seminar – Decision-Oriented Modeling in Building Energy Systems
DESCRIPTION:Abstract\nBuilding energy systems (BESs) serve as crucial flexible resources within power systems. Among various building load types\, thermostatically controlled loads (TCLs) for indoor temperature regulation are the primary contributors to flexible grid services. To harness this flexibility\, it is essential to acquire a thermal dynamics model that represents temperature variations influenced by TCLs. Data-driven methods are commonly employed to capture thermal dynamics accurately\, and model performance is assessed using statistical metrics\, such as mean square error. However\, this approach may lead to biases in model applications because of the unaligned modeling errors and application objectives. In other words\, maximal model accuracy does not necessarily translate to maximal application objectives in terms of actual thermal dynamics within buildings. \nTo tackle this problem\, our research focuses on a new perspective of decision-oriented modeling of thermal dynamics\, ensuring that the modeling objectives are closely aligned with application purposes. We demonstrate the potential of the perspective through two topics: individual building modeling for energy-efficient temperature control\, and multiple building modeling for flexibility aggregation. \nSpeaker\nMr. Cui Xueyuan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nXueyuan Cui received the B.E. degree and the M.S. degree from Zhejiang University\, Hangzhou\, China\, in 2019 and 2022. He is currently a Ph.D. Candidate at the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong\, China. His research interests include grid-interactive buildings\, virtual power plants\, and learning to optimize. \nOrganizer\nProf. Yi Wang \nAll are welcome.
URL:https://ece.hku.hk/events/20241128-2/
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:20241128T090000
DTEND;TZID=Asia/Hong_Kong:20241128T100000
DTSTAMP:20260511T095859
CREATED:20241125T073223Z
LAST-MODIFIED:20250114T032042Z
UID:19483-1732784400-1732788000@ece.hku.hk
SUMMARY:RPG Seminar – Design of High-Performance Microwave Active Circuits Using Synthesis Techniques
DESCRIPTION:Abstract\nMicrowave active circuits\, such as low-noise amplifiers\, power amplifiers\, and oscillators\, are essential components of the transceivers in the front-end of RF communication systems. As modern communication systems evolve\, the demand for higher performance increases. This necessitates compact circuit designs for integration applications\, along with reduced jitter and bit error rates for enhanced communication accuracy. Additionally\, multi-functionality\, such as multi-band operation\, is currently the research hotspot. However\, achieving these high-performance goals with existing circuit design methods\, which are generally based on experience and trial-and-error\, is often time-consuming and lacks a systematic design approach. \nIn this seminar\, we will introduce a series synthesis techniques for microwave active circuits. Using these techniques\, we have developed oscillators with ultra-low phase noise\, an ultra-wideband voltage-controlled oscillator (VCO)\, and dual-band power amplifier circuits. Simulation and measurement results demonstrate that these novel circuits achieve state-of-the-art performance compared to existing designs. \nSpeaker\nMiss Sun Menghan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nMenghan Sun received the B.S. degree from the University of Electronic Science and Technology of China\, Chengdu\, China\, in 2020. She is pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong. Her current research interests include RF/mm-wave circuit designs. \nOrganizer\nProf. Lawrence Yeung \nAll are welcome.
URL:https://ece.hku.hk/events/20241128-6/
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:20241125T150000
DTEND;TZID=Asia/Hong_Kong:20241125T160000
DTSTAMP:20260511T095859
CREATED:20241115T070331Z
LAST-MODIFIED:20250114T032117Z
UID:19452-1732546800-1732550400@ece.hku.hk
SUMMARY:RPG Seminar – Electrically Reconfigurable Surface Acoustic Wave Phase Shifters Based on ZnO TFTs on LiNbO3 Substrate
DESCRIPTION:Abstract\nReconfigurable surface acoustic wave (SAW) phase shifters are emerging as important components for advancing secure wireless communication\, adaptable signal processing\, and intelligent sensing technologies.   A promising approach to SAW modulation involves gate voltage-controlled tuning through acoustoelectric interactions\, which offers efficient modulation at low bias voltages.   However\, current acoustoelectric devices face limitations\, such as restricted tunability\, complex heterostructures\, and demanding fabrication processes\, hindering their practical deployment. \nIn this seminar\, we will introduce a new voltage-tunable SAW phase shifter material system\, based on an atomic layer deposition (ALD) ZnO thin-film transistor (TFT) integrated with a LiNbO3 substrate. This novel structure leverages the high electromechanical coupling coefficient (K²) of LiNbO3 and the superior conductivity tuning capability of ZnO\, while also featuring a simplified architecture that facilitates fabrication.  This ZnO-on-LiNbO3 platform holds promise for a broad range of applications requiring adaptable acoustic components. \nSpeaker\nMr. Zhang Yi\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong\n \nBiography of the speaker\nZhang Yi is a Ph.D. candidate at The University of Hong Kong specializing in the design and fabrication of MEMS-based piezoelectric acoustic wave devices. Zhang holds a Master’s and bachelor’s degrees in engineering from Wuhan University\, where he developed foundational expertise in acoustic wave technology. His current work aims to drive innovations in “sensing-memory-computing” integrated chip systems\, advancing next-generation piezoelectric devices with broad potential in smart and wireless technologies. \nOrganizer\nProf. Han Wang \nAll are welcome.
URL:https://ece.hku.hk/events/20241125-2/
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:20241125T140000
DTEND;TZID=Asia/Hong_Kong:20241125T150000
DTSTAMP:20260511T095859
CREATED:20241115T070011Z
LAST-MODIFIED:20250114T032150Z
UID:19451-1732543200-1732546800@ece.hku.hk
SUMMARY:RPG Seminar – High-performance E-mode GaN HEMTs and Inverters Using a CTL-based Monolithically Integrated Platform for Power ICs Applications
DESCRIPTION:Abstract\nGaN high-electron-mobility transistors (HEMTs) have been extensively studied and commercialized due to their superior material properties for high-frequency and high-power applications. To fully harness their potential\, GaN-based power ICs have been proposed to develop energy-efficient\, high-density integrated circuits and systems. \nTo ensure fail-safe operation\, minimize standby power consumption\, and facilitate circuit simplicity\, enhancement-mode (E-mode) GaN HEMTs with high VTH are required. Moreover\, E-mode GaN HEMTs in direct-coupled FET logic (DCFL) integration show considerable advantages and promising potential for GaN power IC applications. \nIn this seminar\, we will introduce a novel charge trapping layer (CTL)-based monolithically integrated platform to address existing technological challenges and demonstrate high-performance E-mode GaN HEMTs and inverters for power IC applications. This work offers a promising approach for implementing E-mode GaN HEMTs with high VTH and excellent breakdown characteristics without performance degradation. It unlocks their potential for the monolithic integration of GaN HEMTs. \nSpeaker\nMr. Jiang Yang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Jiang Yang received his Bachelor of Science (B.S.) degree from the Southern University of Science and Technology (SUSTech)\, Shenzhen\, China\, in 2019. From 2019 to 2021\, he was with the wide-bandgap semiconductor electronic materials and devices group at SUSTech as a research assistant. He is currently pursuing his Ph.D. degree with the Department of Electrical and Electronic Engineering at The University of Hong Kong. His research interests include GaN-based semiconductor materials and devices\, integrated GaN power device technology\, and emerging in-memory/sensor computing. \nOrganizer\nProf. Han Wang \nAll are welcome!
URL:https://ece.hku.hk/events/20241125-1/
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:20241121T160000
DTEND;TZID=Asia/Hong_Kong:20241121T170000
DTSTAMP:20260511T095859
CREATED:20241119T072932Z
LAST-MODIFIED:20250114T032246Z
UID:19454-1732204800-1732208400@ece.hku.hk
SUMMARY:Integrated Sensing and Communications: From Signal Processing to Prototype
DESCRIPTION:Abstract\nIntegrating sensing functionality into communication devices is emerging as a key feature of the 6G Radio access network. Dual-function radar communication (DFRC) systems implement both sensing and communication using the same hardware thus saving in power\, cost and spectral efficiency. In this talk\, we focus on some of the signal processing aspects of designing and implementing DFRC systems and discuss how the convergence of sensing and communication can be utilized to efficiently exploit congested resources and to communicatee intelligence via sensing.  In particular\, we begin by introducing several approaches to reduce hardware cost by exploiting sub-Nyquist principles and sparse arrays to sense and communicate jointly at low sampling and bit rates. We then introduce new hardware designs that allow continuous monitoring using event-based sampling and high dynamic range. We next consider several different approaches to waveform design and receive signal processing considering both radar detection mode and target localization including spectrum sharing\, joint precoder design\, and index modulation techniques. Our approaches allow design flexibility in trading off radar and communication performance\, while preserving the radar ambiguity function. We end by discussing future trends in DFRC systems including model-based AI for communication and radar under uncertain channels\, near-field communication and radar\, and hybrid RIS/DMA to create configurable radiation patterns for scalable and low power sensing and communication. Throughout the talk we will consider both the theory and hardware prototypes and show several demos of real-time DFRC systems\, low bit and low power ADCs\, and cognitive joint radio and radar systems. \nSpeaker\nProf. Yonina Eldar\nWeizmann Institute of Science\, Israel \nBiography of the Speaker\nYonina Eldar is a Professor in the Department of Mathematics and Computer Science\, Weizmann Institute of Science\, Rehovot\, Israel where she heads the Center for Biomedical Engineering and Signal Processing and holds the Dorothy and Patrick Gorman Professorial Chair. She is also a Visiting Professor at MIT\, a Visiting Scientist at the Broad Institute\, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford.  She is a member of the Israel Academy of Sciences and Humanities\, an IEEE Fellow and a EURASIP Fellow. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering from Tel-Aviv University\, and the Ph.D. degree in electrical engineering and computer science from MIT\, in 2002. She has received many awards for excellence in research and teaching\, including the IEEE Signal Processing Society Technical Achievement Award (2013)\, the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation\, the Weizmann Prize for Exact Sciences\, the Wolf Foundation Krill Prize for Excellence in Scientific Research\, the Henry Taub Prize for Excellence in Research (twice)\, the Hershel Rich Innovation Award (three times)\, and the Award for Women with Distinguished Contributions. She received several best paper awards and best demo awards together with her research students and colleagues\, was selected as one of the 50 most influential women in Israel\, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing\, a member of several IEEE Technical Committees and Award Committees\, and heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241121-3/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/1280-5.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241121T150000
DTEND;TZID=Asia/Hong_Kong:20241121T160000
DTSTAMP:20260511T095859
CREATED:20241115T035452Z
LAST-MODIFIED:20250114T032348Z
UID:19447-1732201200-1732204800@ece.hku.hk
SUMMARY:Data-Centric Architecture and Algorithm Co-design for Data-Intensive Modern Applications
DESCRIPTION:Mode: Online via Zoom\nZoom Link: https://hku.zoom.us/j/97278710788?pwd=VOGfxnayhDjXu3PTxj37IfWDzCGQ1i.1\nMeeting ID: 972 7871 0788\nPassword: 533887 \nAbstract\nIn today’s digital landscape\, the exponential growth of data has become the driving force behind modern applications\, such as genome analysis and machine learning applications\, revolutionizing our approach to healthcare and overall living quality. However\, this unprecedented deluge of data poses a formidable challenge to traditional von Neumann computer architectures. The inefficiencies arising from the constant data movement between processors and memory consume a substantial portion of both execution time and energy when running modern applications on conventional von Neumann computers. To reduce this significant data movement\, data-centric architectures\, particularly processing-in-memory accelerators\, emerge as a promising solution by enabling the processing of data directly where it resides. Nonetheless\, most existing data-centric architectures primarily focus on accelerating specific arithmetic operations\, inadvertently leaving a substantial gap between the architectural enhancements and the holistic needs of modern applications. Concurrently\, conventional software optimizations often treat the architecture as a black box\, which inherently limits the potential acceleration of applications. \nThis talk seeks to bridge the gaps between modern applications and data-centric architectures and revolutionize the landscape of data-centric acceleration. First\, this talk delves into the distinctive features of modern applications\, illustrating these features with a focus on genome analysis within the realm of bioinformatics. Second\, challenges arise when data-intensive modern applications are executed on conventional computers. The talk then transitions to a compelling remedy: the adoption of a data-centric architecture. Third\, this talk outlines the intricacies involved in designing a data-centric architecture for modern applications. It explores the challenges inherent in this process and concurrently offers potential solutions. Following this analysis\, the talk advances to put forth an innovative architecture specifically designed for genome analysis via algorithm-architecture co-design. In conclusion\, the talk wraps up with a summary and a glimpse into future avenues of exploration. \nSpeaker\nDr. Haiyu Mao\nLecturer (Assistant Professor)\,\nDepartment of Engineering\,\nKing’s College London \nBiography of the Speaker\nDr. Haiyu Mao is a Lecturer (Assistant Professor) in the Department of Engineering at King’s College London. Before that\, she was a postdoctoral researcher in the SAFARI Research group led by Prof. Onur Mutlu at ETH Zurich\, Switzerland\, since September 2020. In July 2020\, she received her Ph.D. degree in computer science from Tsinghua University\, China. Her research interests intersect between computer architecture\, memory systems\, data-centric acceleration\, bioinformatics\, machine learning accelerators\, non-volatile memory\, and secure memory. Visit Haiyu’s website for more info: https://hybol1993.github.io \nOrganiser\nProf. Kaibin Huang\nHead\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241121-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241121T143000
DTEND;TZID=Asia/Hong_Kong:20241121T153000
DTSTAMP:20260511T095859
CREATED:20241115T035707Z
LAST-MODIFIED:20250114T032601Z
UID:19449-1732199400-1732203000@ece.hku.hk
SUMMARY:Low-coherence Biophotonics Imaging for the Brain
DESCRIPTION:Abstract\nThis talk will highlight our recent efforts to explore the brain using low-coherence light. We will discuss two key scenarios: intraoperative assessment of brain cancer infiltration in patients and real-time imaging of dynamic neural activities in freely behaving rodents. In the first scenario\, focused on clinical translation\, we developed a quantitative color-coded optical coherence tomography (OCT) technology. This provides neurosurgeons with direct visual cues based on the intrinsic optical properties of tissues\, enabling them to maximize cancer resection while minimizing damage to healthy brain tissue. Our results from over 50 patients demonstrate excellent specificity and sensitivity. For the second scenario\, which pertains to basic research\, we created the first all-fiber-optic\, head-mounted\, ultracompact (~2 mm diameter)\, and ultralight (<1 g) two-photon fiberscopy platform. This allows for high-resolution imaging of neuronal activity in freely walking or rotating mice. We will discuss recent advancements\, including a 15X increase in the area field of view through cascaded magnification\, facilitating the imaging of multiple neurons and their functional correlations within a single frame. Additionally\, we achieved a significant increase in imaging frame rates (10-30 times faster\, reaching video rates) through an improved scanner design and a two-stage deep learning strategy. Detailed neural imaging results will be presented at the seminar\, and if time permits\, we will also explore other applications of these technologies for noninvasive in vivo optical histology. \nSpeaker\nProf. Xingde Li\nDepartment of Biomedical Engineering\,\nJohns Hopkins University \nBiography of the Speaker\nXingde Li earned his PhD in Physics from the University of PENN in 1998. Following 3 years of postdoctoral training at MIT\, he began his academic career as Assistant Professor at the University of Washington. In 2009\, he joined the BME Department at Johns Hopkins as Associate Professor and later became a Full Professor in 2011. His research is centered around on biophotonics imaging technologies and their applications in translational and basic research. He has published about 150 journal papers\, with a total citation over 23\,000 and an H-index of 65 (Google Scholar). Beyond research endeavors\, he has actively participated in various committees for different societies\, chaired numerous international conferences\, and served on many proposal review panels. He has also taken on editorial roles as a topical editor\, associate editor for several journals and the lead founding EiC for a Science Partner Journal – BMEF. He has been elected Fellow of OPTICA\, SPIE\, and AIMBE. \nOrganiser\nProf. Kenneth K.Y. Wong\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241121-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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DTSTART;TZID=Asia/Hong_Kong:20241119T133000
DTEND;TZID=Asia/Hong_Kong:20241119T143000
DTSTAMP:20260511T095859
CREATED:20241030T022835Z
LAST-MODIFIED:20250114T032727Z
UID:19361-1732023000-1732026600@ece.hku.hk
SUMMARY:Towards Provable Unaligned Multimodal Learning: A Model Identification Perspective
DESCRIPTION:The seminar scheduled for November 19\, 2024 (Tue) will start earlier at 1:30 pm\, while the date and venue will remain unchanged.\nAbstract\n2023 was “the year of AI”\, highlighted by the release of numerous AI models with remarkable capabilities. Multimodal learning is at the forefront of AI advancements\, with state-of-the-art models like GPT-4 and Gemini emphasizing multimodal functionalities as their defining features. Despite its importance\, many aspects of multimodal learning\, and AI developments in general\, still lack a concrete and comprehensive understanding—which is essential for building resilient and trustworthy systems. Our research focuses on the understanding of AI/ML systems to drive theory-backed advancements. From this perspective\, this presentation revisits a core component of multimodal learning—Unsupervised Domain Translation (UDT). Many UDT systems\, such as CycleGAN\, use Distribution Matching (DM) modules\, which often fail in content-aligned translations due to measure-preserving automorphism (MPA). Existing remedies fall short of guaranteed performance. In my talk\, I will introduce a model identification perspective for UDT\, overcoming the MPA issues and ensuring identifiability of the desired translation functions. This is the first proven identification result in UDT under CycleGAN’s settings\, to our knowledge. We have also broadened these concepts\, providing solutions for various translation challenges\, enabling provable content-style disentanglement\, and offering more versatile cross-domain data generation. These advancements promise significant theoretically supported enhancements for UDT applications\, particularly in annotation-limited fields such as medicine and biology. \nSpeaker\nProf. Xiao Fu\nAssociate Professor\,\nOregon State University \nBiography of the Speaker\nXiao Fu has been with the School of Electrical Engineering and Computer Science\, Oregon State University since 2017\, where he is currently an Associate Professor. He received the Ph.D. degree in Electronic Engineering from The Chinese University of Hong Kong\, in 2014. He was a Postdoctoral Associate with the Department of Electrical and Computer Engineering\, University of Minnesota – Twin Cities\, from 2014 to 2017. His research interests include the broad area of machine learning and signal processing\, especially theory and algorithms. Dr. Fu received the Best Student Paper Award at ICASSP 2014\, the 2022 IEEE Signal Processing Society (SPS) Best Paper Award\, and the 2022 IEEE SPS Donald G. Fink Overview Paper Award. He also received the Outstanding Postdoctoral Scholar Award at University of Minnesota in 2016\, the Engelbrecht Early Career Faculty Award from the College of Engineering at Oregon State University in 2023\, and the National Science Foundation (NSF) CAREER Award in 2022. \nOrganiser\nProf. Kaibin Huang\nHead of Department\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241119-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241113T134000
DTEND;TZID=Asia/Hong_Kong:20241113T161000
DTSTAMP:20260511T095859
CREATED:20241108T033137Z
LAST-MODIFIED:20250114T032825Z
UID:19425-1731505200-1731514200@ece.hku.hk
SUMMARY:Mini-Workshop on “Frontiers of Geometry Computing & Visual Media 2024”
DESCRIPTION:You are cordially invited to join us for the Mini-Workshop on “Frontiers of Geometry Computing & Visual Media 2024”\, organised by the Department of Electrical & Electronic Engineering and Computational Imaging & Mixed Representation Laboratory\, The University of Hong Kong. The workshop aims to encourage innovative spirit\, promote excellence and sustain quality\, strive for improvement\, and connect communities. \nAll HKU staff and students are welcome to join the workshop! \nFor more details about the workshop and the speakers\, please visit the following website: \nhttps://hku.welight.fun/events/workshop_24Nov
URL:https://ece.hku.hk/events/20241113-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241108T143000
DTEND;TZID=Asia/Hong_Kong:20241108T152000
DTSTAMP:20260511T095859
CREATED:20241030T040743Z
LAST-MODIFIED:20250220T074437Z
UID:19375-1731076200-1731079200@ece.hku.hk
SUMMARY:Engineering Graduate Recruitment Talk - EMSD
DESCRIPTION:Opportunity Alert! Are you ready to jumpstart your career as an Electrical Engineering Graduate at EMSD? Join us for an engaging session with a dynamic Engineer from EMSD who will share valuable insights on the available positions and the recruitment process.
URL:https://ece.hku.hk/events/20241108-1/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Career Talks,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241108T100000
DTEND;TZID=Asia/Hong_Kong:20241108T110000
DTSTAMP:20260511T095859
CREATED:20241104T080109Z
LAST-MODIFIED:20250114T033258Z
UID:19423-1731060000-1731063600@ece.hku.hk
SUMMARY:The Sound of Music at the Nanoscale – Exploring the Nanoscale World with NEMS Resonators Based on Low Dimensional Nanomaterials
DESCRIPTION:Abstract\nThe advent of low-dimensional nanostructures has enabled a plethora of new devices and systems. Among them\, nanoelectromechanical systems (NEMS) offers the unique capability of coupling the exquisite material properties found in these atomically-defined nanostructures with their mechanical degree of freedom\, opening new opportunities for exploring exotic phenomena at the nanoscale. In particular\, as these devices driven into mechanical vibration—just as musical instruments—they become essentially nanoscale guitars\, drums\, tuning folks\, etc. By studying the infinitesimal mechanical vibrations in these nanoscale “music instruments”\, i.e.\, listening to the “sound of music” at the nanoscale\, researchers can study a number of fundamental physical processes such as absorption\, phase transition\, anisotropy\, and nonlinear processes. \nSpeaker\nProf. Zenghui Wang\nInstitute of Frontier and Fundamental Sciences\,\nUniversity of Electronic Science and Technology of China \nBiography of the Speaker          \nZenghui Wang is currently a professor in the Institute of Frontier and Fundamental Sciences (IFFS) at the University of Electronic Science and Technology of China (UESTC). His research interests and expertise primarily focus on nanoscale devices and systems\, particularly Nanoscale Resonators\, and High-Frequency Resonant Sensors & Transducers. Prior to joining Case\, during 2010-2012\, he worked at Cornell University as a postdoc researcher. He earned a Ph.D. degree (2010) from University of Washington\, Seattle\, for building an ultra-high frequency NEMS resonant sensor with an individual single-walled carbon nanotube\, and using it to detect and study the low-dimensional phase transitions of the atomic layer adsorbed on the nanotube surface. He is an expert on studies of emerging nanoscale devices and sensors based on new materials such as carbon nanotubes\, graphene\, and other low-dimensional nanomaterials\, and has published 20+ research articles in peer-reviewed journals\, including Science\, Nature Physics\, Nature Nanotechnology\, Nature Communications\, Science Advances\, Nano Letters\, ACS Nano\, Physical Review Letters\, 2D Materials\, etc.\,. He has given dozens of invited talks and seminars at peer-reviewed conferences and research universities. He is an Associate Editor for Micro and Nano Letters\, and has been serving on the Technical Program Committees for IEEE IFCS\, IEEE Nano\, and the MEMS/NEMS Technical Group at the American Vacuum Society (AVS) International Symposium and Exhibition. \nOrganiser\nProf. Han Wang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nCo-organiser:\nIEEE ED/SSC Hong Kong Joint Chapter \nAll are welcome! 
URL:https://ece.hku.hk/events/20241108-2/
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/2024/11/34343.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241107T141500
DTEND;TZID=Asia/Hong_Kong:20241107T153000
DTSTAMP:20260511T095859
CREATED:20241016T092204Z
LAST-MODIFIED:20250114T033454Z
UID:19313-1730988900-1730993400@ece.hku.hk
SUMMARY:Collaboration and Evolution of Foundation and Specialized Models
DESCRIPTION:The seminar scheduled for 24 October 2024 (Thur) has been postponed to 7 November 2024 (Thur)\, and time and venue remain unchanged.\n\n \n\nAbstract\nThe prevailing GPU resource monopoly significantly restricts AI development\, confining participation in the pretraining stages of Large Language Models (LLMs) to a few researchers. This project introduces a novel system that integrates hundreds of domain-specific models to construct a foundational model for Artificial General Intelligence (AGI) with minimal computational demand. By employing smaller\, efficient models\, leveraging top-ranked models across diverse domains through a robust ranking algorithm\, and continuously optimizing the evolving foundation model\, this approach seeks to democratize AI development. It shifts from the traditional ‘model over data’ method to a ‘model over models’ strategy\, aiming to reduce reliance on extensive computational resources and promote broader innovation and inclusivity in AI. \nSpeaker\nProf. Hongxia Yang\nProfessor\,\nDepartment of Computing\,\nThe Hong Kong Polytechnic University \nBiography of the Speaker\nProf. Hongxia Yang\, with over 15 years of experience as an AI scientist\, specializes in large-scale machine learning\, data mining\, and deep learning. Throughout her career\, she has developed 10 significant algorithmic systems\, improving the operations of various enterprises. Her research includes pre-trained models\, big data analytics\, and the practical deployment of large language model(LLM) systems in real settings. Prof. Yang has published more than 100 top-tier papers\, amassed around 10K citations with an H-index of 44\, and holds over 50 patents. She has received several awards\, including the 2019 SAIL Award at the World Artificial Intelligence Conference and the 2020 National Science and Technology Progress Award\, China’s top tech accolade. Named one of Forbes China’s Top 50 Women in Tech in 2022 and AI 2000 Most Influential Scholar Award in 2023-2024\, Prof. Yang has held prominent roles at ByteDance US\, Alibaba Group\, Yahoo! Inc\, and IBM T.J. Watson Research Center. She earned her PhD from Duke University and her B.S. from Nankai University.\n\nOrganiser\nProf. N. Wong\nAssociate Professor\,\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20241024-1/
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/2024/10/1280-1.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241107T130000
DTEND;TZID=Asia/Hong_Kong:20241107T153000
DTSTAMP:20260511T095859
CREATED:20241029T064840Z
LAST-MODIFIED:20250114T033534Z
UID:19352-1730984400-1730993400@ece.hku.hk
SUMMARY:Felix Wu Distinguished Lecture in Power Systems: Change of Paradigm of Power System Operation and Control
DESCRIPTION:Abstract\nSince the inception at the end of the 19th Century\, technical characteristics of power systems have been determined by the physics of synchronous machines (SMs) that convert the primary energy produced by thermal/hydro/nuclear power stations into electricity. However\, the traditional power plants are increasingly being replaced by wind/PV plants and batteries which are connected to the grid asynchronously by means of power electronics (controllable inverters). This means that the power system technical characteristics are increasingly being determined by the control algorithms of inverters rather than the physics of SMs\, and this has profound consequences for power system operation and control. The presentation will discuss those changes and especially the question to what extent inverters can replace synchronous machines. \nSpeaker\nProf. Janusz Bialek\nPrincipal Research Fellow\,\nImperial College London \nBiography of the Speaker\nProfessor Janusz Bialek (FIEEE) is Principal Research Fellow at Imperial College London. Previously\, he held Chair positions at the University of Edinburgh\, Durham University\, Newcastle University and Skolkovo Institute of Science and Technology (Skoltech). Janusz has been PI and Col of multi-million research grants funded by UK research councils and the industry\, and a consultant to the UK government\, European Commission\, and International Energy Agency. He has published widely on integration of renewable generation in power systems\, smart grids\, power system dynamics\, preventing electricity blackouts and power markets. His current main research interests are in addressing the techno-economic challenges posed by increasing penetration of wind/PV/batteries and other devices that are connected to the grid by means of power electronics (programmable inverters)\, rather than synchronous machines\, therefore changing fundamental technical characteristics of the power system. \nOrganiser\nProf. Y. Hou\nDeputy Head\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nCo-organiser\nTam Wing Fan Innovation Wing Two \nAll are welcome! \nDirection to Innovation Wing Two: https://innowings.engg.hku.hk/innowing2/visitors
URL:https://ece.hku.hk/events/20241107-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/10/1280.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241104T110000
DTEND;TZID=Asia/Hong_Kong:20241104T120000
DTSTAMP:20260511T095859
CREATED:20241029T071514Z
LAST-MODIFIED:20250114T033626Z
UID:19354-1730718000-1730721600@ece.hku.hk
SUMMARY:Fundamental Principle of Magnetic Levitation for AI Driven Maglev Rides
DESCRIPTION:  \nSeminar on “Fundamental Principle of Magnetic Levitation for AI Driven Maglev Rides” \nDear Colleagues and Students\, \nYou are cordially invited to join the upcoming seminar on “Fundamental Principle of Magnetic Levitation for AI Driven Maglev Rides”. Please find the details below. \nDate: 4 November 2024 (Monday)\nTime: 11:00 am – 12:00 noon (HKT)\nVenue: Room 603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong\nZoom: https://hku.zoom.us/j/97401048897?pwd=WS8LiUbmLYOCce4bIWW8nHbHSDrJg0.1\nMeeting ID: 974 0104 8897\nPassword: 980783\n\nSpeaker\nProf. Joseph Y Hui\nArizona State University \nModerators\nProf. Victor O.K. Li & Prof. Jacqueline C.K. Lam\nDirector & Co-director\,\nHKU-Cambridge AI-WiSe \nAbstract\nElectric propulsion was invented by Michael Faraday who laid the foundation of electric locomotion. Subsequent effort for magnetic levitation has failed to replace wheels as the primary means of reducing impedance to locomotion. My thesis is that levitation should start from Ampere’s law:\n\nBased on this first principle\, I invented and patented a method that will make cars obsolete. Personal AI driven maglev is small\, simple\, silent\, smooth\, speedy\, safe\, smart\, and saving time\, energy and the environment with zero carbon emission. I will demonstrate a working maglev hoverboard and vehicle to show you how personal maglev will change the World. We propose building personal maglev for the Northern Metropolis. We can also increase China’s High Speed Rail throughput 10X and decrease cost to passenger 10X using existing rail infrastructures and individualized AI driven maglev carriages to transport passengers end-to-end without fixed train schedule. \nBiography of the Speaker\nJoseph Y Hui matriculated in 1977 and won the IIE scholarship to attend MIT\, where he earned his BS’81\, MS’81\, EE’82\, and PhD’83 degrees\, all in EECS. He has worked at Comsat\, Bell Labs\, Bellcore\, and IBM. He taught at Columbia University (86-89)\, Rutgers University (89-99)\, Chinese University (95-99)\, and ASU (99 – now). He works on his AWESOME inventions for clean Air\, Water\, and Energy as well as fast and safe travel by Space\, Ocean\, Maglev\, and Eviation. He is an IEEE fellow\, NSF Presidential Young Investigator\, and member of US Academy of Inventors with tens of patents granted.\n\nLooking forward to welcoming you at the seminar next Monday. \nBest Regards\,\nVictor O.K. Li & Jacqueline C.K. Lam\nDepartment of Electrical and Electronic Engineering
URL:https://ece.hku.hk/events/20241104-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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