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TZID:Asia/Hong_Kong
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250313T103000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260510T025208
CREATED:20250102T023031Z
LAST-MODIFIED:20250211T042315Z
UID:19666-1741861800-1741865400@ece.hku.hk
SUMMARY:Quantum Technologies with Hexagonal Boron Nitride
DESCRIPTION:Abstract\nEngineering robust\, solid-state quantum systems is amongst the most pressing challenges to realise scalable quantum photonic circuitry. While several 3D systems (such as diamond or gallium arsenide) have been thoroughly studied\, solid state emitters in van der Waals (vdW) and two dimensional (2D) materials are still in their infancy. \nIn this presentation\, I will discuss the appeal of an emerging vdW crystal – hexagonal boron nitride (hBN). This unique system possesses a large bandgap of ~ 6 eV and can host single defects that can act as ultra-bright quantum light sources. In addition\, some of these defects exhibit spin dependent fluorescence that can be initialised and coherently manipulated. I will discuss in details various methodologies to engineer these defects and show their peculiar properties. Furthermore\, I will discuss how hBN crystals can be carefully sculpted into nanoscale photonic resonators to confine and guide light at the nanoscale. Taking advantage of the unique 2D nature of hBN\, I will also show promising avenues to integrate hBN emitters with silicon nitride photonic crystal cavities. \nAll in all\, hBN possesses all the vital constituents to become the leading platform for integrated quantum photonics. To this extent\, I will highlight the challenges and opportunities in engineering hBN quantum photonic devices and will frame it more broadly in the growing interest with 2D materials nanophotonics. \nSpeaker\nProf. Igor Aharonovich\nSchool of Mathematical and Physical Sciences\,\nFaculty of Science\,\nUniversity of Technology Sydney \nBiography of the Speaker\nIgor Aharonovich is an award-winning scientist working on cutting-edge research into quantum sources that are able to generate\, encode and distribute quantum information. A Professor in the School of Mathematical and Physical Sciences at UTS\, Igor investigates optically active defects in solids\, with the aim of identifying a new generation of ultra-bright solid state quantum emitters. He is a chief investigator at the ARC Centre of Excellence for Transformative Meta-Optical Materials (TMOS)\, and leads an international collaboration investigating the chemical structure of crystal imperfections\, or defects\, in the nanomaterial hexagonal boron nitride (hBN). \nIn 2016\, Igor and his team discovered the first quantum emitters in 2D materials that operate at room temperature based on defects in hBN. He has co-authored more than 200 peer-reviewed publications\, including one of the most cited reviews on diamond photonics. He has also written a road map for solid state single-photon sources. In 2019\, Igor co-founded the inaugural online photonics conference\, Photonics Online Meetup\, which attracted more than 1100 attendees from around the world\, and which was highlighted by top science outlets. The conference now runs twice a year. He has received several international awards including the Pawsey Medal (2017)\, the IEEE Photonics Young Investigator Award (2016) and in 2020 he was the recipient of the Kavli Foundation Early Career Lectureship in Materials Science from Materials Research Society. In 2021\, he became a Fellow of the Optical Society (OSA)\, and in 2024 elected as a fellow of SPIE.\nRead more about the speaker’s biography: https://profiles.uts.edu.au/Igor.Aharonovich \nOrganiser\nProf. Zhiqin Chu\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nSupported by\nTam Wing Fan Innovation Wing Two \nAll are welcome! \nDirection: https://innowings.engg.hku.hk/innowing2/visitors
URL:https://ece.hku.hk/events/20250313-1/
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250313T100000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260510T025208
CREATED:20250211T081812Z
LAST-MODIFIED:20250212T083236Z
UID:108702-1741860000-1741865400@ece.hku.hk
SUMMARY:In-memory\, Mixed Analog-digital Architectures for Energy-efficient Computing Applications
DESCRIPTION:Abstract\nThere is simultaneously an interest for more energy-efficient hardware in challenging applications\, as well as a drive to overhaul the von Neumann architecture toward more brain-like architectures. Compute-in-Memory (CIM) is one emerging paradigm addressing key memory bottlenecks such as bandwidth limitations\, access latency\, and high data movement energy in conventional computing platforms.\nIn part one\, we use this paradigm to accelerate the solving of challenging optimization problems. We describe our in-memory\, hardware approach built around modified Hopfield neural networks to accelerate problem classes such as Boolean satisfiability (3-SAT). In an algorithm-hardware co-design process\, we have developed three different architectures steadily improving on the state-of-the-art. We discuss issues encountered in mapping native optimization problems to physical hardware\, precision demands\, and matching mixed analog-digital blocks to the algorithmic needs. Quantitative performance comparisons to competing approaches in both mature and emerging technologies will be presented.\nIn the second part of the talk\, we extend CIM beyond matrix-dominant workloads\, exploring its potential for novel and powerful models like Kolmogorov-Arnold Networks (KAN). Our new work\, called “KA-CIM\,” provides hardware acceleration for KANs. KANs offer significant parameter reduction over traditional neural networks for AI+Science applications but relies on computationally expensive non-linear functions. We present an innovative memory-centric design that enables energy-efficient and flexible computation of non-linear functions central to KAN\, while efficiently executing KAN inference.\nWe also discuss analog CIM using commodity DRAM architectures. Unlike SRAM- and emerging memory-based CIM accelerators\, DRAM-based CIM offers both high memory capacity and technological maturity\, making it an attractive candidate for large-scale AI workloads. This portion of the talk will present both the opportunities and constraints of using commodity DRAM for CIM. A novel analog CIM architecture will be presented\, which mitigates several of these constraints and demonstrates how area- and power-intensive ADCs can be efficiently integrated within an area-optimized commodity DRAM design. Furthermore\, a key feature of this architecture is its Dual-Mode functionality\, enabling it to seamlessly operate as both conventional main memory and an accelerator. \nSpeakers\nProf. John Paul Strachan\nProfessor\, RWTH Aachen University\, Aachen\, Germany\nHead\, Peter Grünberg Institute (PGI-14)\, Forschungszentrum Jülich\, Jülich\, Germany\n\nDr. Chirag Sudarshan\nPostdoctoral Researcher\nPeter Grünberg Institute (PGI-14)\, Forschungszentrum Jülich\, Jülich\, Germany \nBiography of the Speakers\nProf. John Paul Strachan directs the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14) at Forschungszentrum Jülich and is a Professor at RWTH Aachen.  Previously he led the Emerging Accelerators team as a Distinguished Technologist at Hewlett Packard Labs\, HPE. His teams explore novel types of hardware accelerators using emerging device technologies\, with expertise spanning materials\, device physics\, circuits\, architectures\, benchmarking and building prototype systems. Their interests span applications in machine learning\, network security\, and optimization. John Paul has degrees in physics and electrical engineering from MIT and a PhD in applied physics from Stanford University. He has over 60 patents\, has authored or co-authored over 100 peer-reviewed papers\, and been the PI in many USG research grants. He has previously worked on nanomagnetic devices for memory for which he was awarded the Falicov Award from the American Vacuum Society\, and has developed sensing systems for precision agriculture in a company which he co-founded. He serves in professional societies including IEEE IEDM ExComm\, the Nanotechnology Council ExComm\, and past program chair and steering member of the International Conference on Rebooting Computing.\n\nDr. Chirag Sudarshan is currently a Postdoctoral Researcher at the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14)\, Forschungszentrum Jülich\, working under the supervision of Prof. John Paul Strachan. He received his Master’s degree (2017) and Ph.D. (2023) in Electrical Engineering from the University of Kaiserslautern-Landau\, Germany. During his Ph.D.\, he extensively worked on novel DRAM architectural designs and is now developing innovative compute-in-memory architectures with emerging memory technologies for neuromorphic applications. His contributions have been recognized with a special academic achievement award from the Department of Electrical and Computer Engineering and WIPOTEC GmbH following his master’s studies. He has authored or co-authored 23 publications\, filed six patents\, and currently serves as a reviewer for journals such as Results in Engineering and Micromachines. His research interests include compute-in-memory architectures\, neuromorphic computing\, emerging memory technologies\, and DRAM architectures. \nOrganiser\n\nProf. Can Li\, Department of Electrical and Electronic Engineering\, The University of Hong Kong; and\nCenter for Advanced Semiconductor and Integrated Circuits\n\nAll are welcome! 
URL:https://ece.hku.hk/events/20250313-2/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250307T093000
DTEND;TZID=Asia/Hong_Kong:20250307T223000
DTSTAMP:20260510T025208
CREATED:20250304T072753Z
LAST-MODIFIED:20250306T015705Z
UID:110555-1741339800-1741386600@ece.hku.hk
SUMMARY:Revolutionizing Power Electronics with Heterogeneous Integration
DESCRIPTION:Abstract\nTraditional power electronic equipment has long relied on discrete active and passive components\, with performance enhancements often requiring trade-offs. Despite technological advancements\, manufacturing processes remain labor-intensive and largely unchanged for decades. \nThe advent of wide-bandgap (WBG) power semiconductor devices\, such as silicon carbide (SiC) and gallium nitride (GaN)\, has significantly reduced conduction and switching losses compared to silicon-based counterparts. However\, current design methodologies primarily follow a ‘plug-and-play’ approach\, yielding only incremental improvements in efficiency and power density without fully leveraging the transformative potential of these technologies. \nThis presentation explores the integration of matrix magnetics with WBG power devices to drive a fundamental shift in power electronics design and manufacturing through heterogeneous integration. This holistic approach enables simultaneous enhancements in efficiency\, power density\, cost\, and electromagnetic interference (EMI) performance. Additionally\, it streamlines traditionally labor-intensive manufacturing processes—particularly those involving magnetics and system assembly—through automation. \nThe discussion will feature multiple research examples demonstrating heterogeneous integration of matrix magnetics in power converters across diverse applications and power ranges. These include high-frequency power converters for artificial intelligence (AI) and high-performance computing systems\, battery chargers for electric vehicles\, and solid-state transformers for DC power distribution. \n\nSpeaker\nProf. Qiang LI\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. He is also a recipient of the U.S. National Science Foundation (NSF) Career Award. \nOrganiser\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250307-2/
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/2025/03/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250217T150000
DTEND;TZID=Asia/Hong_Kong:20250217T163000
DTSTAMP:20260510T025208
CREATED:20250211T013747Z
LAST-MODIFIED:20250211T042315Z
UID:108654-1739804400-1739809800@ece.hku.hk
SUMMARY:Personalized Federated Learning and Its Application in 360-degree Video Streaming
DESCRIPTION:Abstract\nFederated learning is a distributed artificial intelligence framework\, which allows multiple edge devices to train a single model collaboratively. In this talk\, we first introduce a personalized federated learning algorithm which can tackle the issues of data heterogeneity and device heterogeneity. Then\, we present a content-based viewport prediction framework for 360-degree video streaming\, wherein users’ head movement prediction models are trained using a personalized federated learning algorithm. The output of the viewport prediction framework corresponds to which video tiles to be transmitted. Finally\, we present an algorithm to determine the bitrate and beamforming matrices in a THz-enabled 360-degree video streaming system with multiple access points. \nSpeaker\nProf. Vincent Wong\nProfessor\nDepartment of Electrical and Computer Engineering\nUniversity of British Columbia\, Canada \nBiography of the Speaker\nVincent Wong is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia\, Vancouver\, Canada. His research areas include protocol design\, optimization\, and resource management of communication networks\, with applications to the Internet\, wireless networks\, smart grid\, mobile edge computing\, and Internet of Things. Dr. Wong is the Editor-in-Chief of the IEEE Transactions on Wireless Communications. He is a Fellow of the IEEE and the Engineering Institute of Canada. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250217-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250214T150000
DTEND;TZID=Asia/Hong_Kong:20250214T160000
DTSTAMP:20260510T025208
CREATED:20250210T095554Z
LAST-MODIFIED:20250211T042315Z
UID:108650-1739545200-1739548800@ece.hku.hk
SUMMARY:Ubiquitous Sensing in 6G Cellular Networks
DESCRIPTION:Abstract\nRecently\, the International Telecommunication Union (ITU) has identified integrated sensing and communication (ISAC) as a primary usage scenario for the sixth-generation (6G) cellular networks in IMT-2030 Framework. As a result\, future cellular networks will provide not only communication services\, but also sensing services such as localization and tracking. However\, how to exploit the existing communication infrastructure to effectively achieve sensing functions remains an open problem for 6G. In this talk\, we will introduce the methodologies to leverage various types of communication nodes in cellular networks as anchors\, including base stations\, user equipments\, and intelligent reflecting surfaces\, to perform ubiquitous sensing. Specifically\, the advantages and disadvantages of each type of anchors will be listed\, and the efficient solutions to overcome these disadvantages will be outlined. Apart from theoretical works\, this talk will also present our latest achievements in building a 6G ISAC platform that operates at the millimeter-wave band. We will conclude this talk by discussing some promising future directions that will be beneficial to the transformation of the world’s largest communication network into the world’s largest sensing network. \nSpeaker\nDr. Liang LIU\nAssociate Professor\, Department of Electrical and Electronic Engineering\, The Hong Kong Polytechnic University \nBiography of the Speaker\nLiang Liu is currently an Associate Professor with the Department of Electrical and Electronic Engineering\, The Hong Kong Polytechnic University. He obtained his Ph.D. degree from National University of Singapore in 2014. His research interests lie in 5G/6G technologies\, including integrated sensing and communication (ISAC)\, massive Internet-of-Things (IoT) connectivity\, etc. Currently\, his project about 6G ISAC is supported by the RGC Collaborative Research Fund (CRF) Young Collaborative Research Grant. \nLiang Liu is an IEEE Communications Society (ComSoc) Distinguished Lecturer. He is a recipient of the 2021 IEEE Signal Processing Society (SPS) Best Paper Award\, the 2017 IEEE SPS Young Author Best Paper Award\, the Best Student Paper Award of 2022 IEEE International Conference on Acoustics\, Speech\, and Signal Processing (ICASSP)\, and the Best Paper Award of the 2011 International Conference on Wireless Communications and Signal Processing. He was recognized by Clarivate Analytics as a Highly Cited Researcher in 2018. He is an Editor of IEEE Transactions on Wireless Communications\, and was a Leading Guest Editor of IEEE Wireless Communications Special Issue on Massive Machine-Type Communications for IoT. He is a co-author of the book “Next Generation Multiple Access” published by Wiley-IEEE Press. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250214-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/22222355.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250213T160000
DTEND;TZID=Asia/Hong_Kong:20250213T170000
DTSTAMP:20260510T025208
CREATED:20250210T095339Z
LAST-MODIFIED:20250211T042315Z
UID:108646-1739462400-1739466000@ece.hku.hk
SUMMARY:6G Waveforms-Perspectives on Throughput\, Reliability\, and ISAC
DESCRIPTION:Online via Zoom link: https://hku.zoom.us/j/2025021302 \nAbstract\nWith the commercialization of 5G technology\, research on 6G has emerged as a key focus in the field of wireless communications. In this talk\, we explore three candidate waveforms for 6G\, designed to meet its stringent requirements for throughput\, reliability\, and integrated sensing and communications (ISAC). \nWe begin by discussing faster-than-Nyquist (FTN) signaling\, a promising technique for enhancing communication spectral efficiency. The unique challenges associated with equalization and channel coding in FTN systems are highlighted\, along with novel solutions that are benchmarked against theoretical performance limits. \nNext\, we examine orthogonal time frequency space (OTFS) modulation\, which enhances communication reliability in dynamic wireless channels. We demonstrate that OTFS introduces a novel coupling mechanism between information symbols and the wireless channel\, enabling efficient equalization and robust MIMO transmissions by fully exploiting channel dynamics. \nFinally\, we focus on a communication-centric ISAC waveform\, evaluating its sensing performance through ambiguity functions. We analytically prove that OFDM is the optimal waveform for minimizing sidelobes in ranging\, while single-carrier waveforms are superior for Doppler sensing when using practical communication signals. \nThe talk concludes with a discussion of potential future research directions in 6G waveform design\, highlighting open challenges and opportunities in this evolving field. \nSpeaker\nDr. Shuangyang Li\nResearch Assistant\, Faculty of Electrical Engineering and Computer Science\, Technical University of Berlin \nBiography of the Speaker\nShuangyang Li (Member\, IEEE) received the B.S.\, M.S.\, and Ph.D. degrees from Xidian University\, China\, in 2013\, 2016\, and 2021\, respectively. He received his second Ph.D. degree from the University of New South Wales (UNSW)\, Australia\, in 2022. He is a recipient of the Marie Skłodowska-Curie Actions (MSCA) fellowship 2022 and is currently a research assistant at the Technical University of Berlin (TU-Berlin). Prior to that\, he was a research associate at the University of Western Australia (UWA). He received the Best Paper Award from IEEE ICC 2023\, and the Best Workshop Paper Award from IEEE WCNC 2023. He was listed in the World’s Top 2% Scientists by Stanford University for citation impact 2024 and is the recipient of the best young researcher award 2024 from the IEEE ComSoc EMEA region. He frequently serves as the organizer/chair for workshops and tutorials on related topics of orthogonal time frequency space (OTFS) in IEEE flagship conferences and is a founding member and currently the co-chair of the special interest group (SIG) on OTFS. He is now an editor of IEEE Transactions on Communications. His research interests include signal processing\, channel coding\, applied information theory\, and their applications to communication systems\, with a specific focus on waveform designs. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250213-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/23423556.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250213T143000
DTEND;TZID=Asia/Hong_Kong:20250213T153000
DTSTAMP:20260510T025208
CREATED:20250210T094926Z
LAST-MODIFIED:20250211T042315Z
UID:108642-1739457000-1739460600@ece.hku.hk
SUMMARY:Bridging Minds\, Not Just Devices: Semantic and Goal-Oriented Communication for the Internet of Intelligent Things
DESCRIPTION:Abstract\nThe next frontier of the Internet of Things (IoT) lies in transforming today’s smart devices into collaborative cognitive agents – an ecosystem termed the Internet of Intelligent Things (IoIT). While current IoT systems center on raw data exchange\, they fall short of enabling true collaboration: devices cannot share meaningful insights or align their objectives across dynamic\, real-world tasks. This talk presents a paradigm shift – semantic and goal-oriented communication – as the critical enabler for IoIT. I will introduce a theoretical framework that conceptualizes semantic communication through two key challenges: language exploitation and language design. The language exploitation problem focuses on optimizing the encoding and decoding of semantics to minimize distortion without modifying the underlying semantic language. In contrast\, the language design problem seeks to co-optimize both the encoder and decoder through joint source-channel coding\, particularly leveraging deep learning-based approaches. The talk will also explore the role of large language models in learning adaptive semantic representations\, making communication systems more resilient and context-aware. Finally\, I will discuss how the goal-oriented principle broadens classical Shannon theory by integrating decision-making objectives into communication system design. By framing communication as a meaning-driven\, goal-aware process\, we usher in a new era of collective intelligence – one where smart devices evolve into collaborative cognitive agents capable of shared understanding and coordinated action. \nSpeaker\nDr. Yulin SHAO\nAssistant Professor\, State Key Laboratory of Internet of Things for Smart City\, University of Macau \nBiography of the Speaker\nDr. Yulin Shao is an Assistant Professor with the State Key Laboratory of Internet of Things for Smart City\, University of Macau\, and a Visiting Researcher with the Department of Electrical and Electronic Engineering\, Imperial College London. He received the B.S. and M.S. degrees in Communications and Information Engineering (Hons.) from Xidian University\, China\, in 2013 and 2016\, and the Ph.D. degree in Information Engineering from the Chinese University of Hong Kong in 2020. He was a Research Assistant with the Institute of Network Coding\, a Visiting Scholar with the Research Laboratory of Electronics at Massachusetts Institute of Technology\, a Research Associate with the Department of Electrical and Electronic Engineering at Imperial College London\, and a Lecturer in Information Processing with the University of Exeter. He was a Guest Lecturer at 5G Academy Italy and IEEE Information Theory Society Bangalore Chapter. \nDr. Shao’s research interests include coding and modulation\, machine learning\, and stochastic control. He is a Series Editor of IEEE Communications Magazine in the area of Artificial Intelligence and Data Science for Communications\, an Editor of IEEE Transactions on Communications in the area of Machine Learning and Communications\, and an Editor of IEEE Communications Letters. He received the Best Poster Award at CIE Information Theory Society 2024\, and the Best Paper Awards at IEEE International Conference on Communications (ICC) 2023 and IEEE Wireless Communications and Networking Conference (WCNC) 2024. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250213-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/2342544.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250210T110000
DTEND;TZID=Asia/Hong_Kong:20250210T120000
DTSTAMP:20260510T025208
CREATED:20250210T094548Z
LAST-MODIFIED:20250211T042315Z
UID:108637-1739185200-1739188800@ece.hku.hk
SUMMARY:Advanced Photonic Thin Films and Nanostructures for Next Generation Optoelectronic Systems
DESCRIPTION:Abstract\nPhotonic materials are the backbones of optical communication\, sensing and imaging systems. The advent of artificial intelligence\, internet of things and human-machine interfaces require optical information perception\, data communication and storage with a much higher bandwidth\, smaller footprint yet extremely low power consumption. Bulk materials can no longer support these tasks. Development of advanced photonic thin films and nanostructures becomes the key challenge. In this report\, I will introduce our recent progress on advanced photonic thin films and nanostructures for silicon photonic and free-space optoelectronic systems. I will cover two topics. First\, magneto-optical nonreciprocal photonics for silicon photonics\, including the development of wafer-scale high quality MO thin films\, nanophotonic structures\, nonreciprocal photonic devices and their application in laser module\, silicon photonic FMCW LiDAR systems. Second\, active optical metasurfaces\, including the development of phase change materials\, ferroelectric thin films and optical metasurfaces for optical switching and imaging applications.\n \nSpeaker\nProf. Lei Bi\nProfessor\, Department of Electronic Science and Engineering\,\nUniversity of Electronic Science and Technology of China (UESTC) \nBiography of the Speaker\nLei Bi is a professor in the department of Electronic Science and Engineering of University of Electronic Science and Technology of China (UESTC). He received his B.S. and M.S. degrees in Tsinghua University in 2004 and 2006 respectively\, both majored in materials science. He received his Ph.D. degree in MIT in 2011\, majored in materials science and engineering. He joined UESTC as a professor in 2013. His research interest includes nonreciprocal photonics\, magneto-photonics and optical metasurface. He has authored or co-authored more than 150 papers in peered-viewed journals. He is a senior member of IEEE\, and a member of Optica and SPIE. \nOrganisers\nProf. Han Wang\, Department of Electrical and Electronic Engineering\, HKU\nCenter for Advanced Semiconductors and Integrated Circuits \nAll are welcome!
URL:https://ece.hku.hk/events/20250210-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/34343425.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250120T110000
DTEND;TZID=Asia/Hong_Kong:20250120T120000
DTSTAMP:20260510T025208
CREATED:20250116T025553Z
LAST-MODIFIED:20250211T042315Z
UID:108129-1737370800-1737374400@ece.hku.hk
SUMMARY:Edges Empowering AI\, Embracing LLMs: Issues\, Technologies\, and Applications
DESCRIPTION:Abstract\nAccelerated by the rapid advancements in AI and IoT technologies\, there is an urgent need to extend AI capabilities to the network edge to fully harness the potential of big data. To address this demand\, edge Intelligence has emerged as a promising paradigm for enabling distributed\, computation-intensive AI applications on edge devices. This talk explores the key dimensions of edge intelligence: data\, models\, and systems. It involves data evaluation by analyzing its contribution to model performance and investigates strategies for optimizing edge models in dynamic edge environments. Special attention is given to technologies such as distributed training (federated edge learning as an example)\, inference acceleration\, and model compression tailored for edge deployments. Furthermore\, with the advent of large language models (LLMs) and their overwhelming computational requirements\, the talk examines the evolving role of edge intelligence. Some open questions remain: e.g.\, how edge systems can integrate with and complement these foundational models\, addressing challenges such as resource constraints and latency while exploring potential synergies in hybrid edge-cloud architectures. \nSpeaker\nProf. Yinglei Teng\nProfessor\,\nBeijing University of Posts and Telecommunications \nBiography of the Speaker\nYinglei Teng\, a professor at Beijing University of Posts and Telecommunications\, specializes in wireless communications\, stochastic optimization and edge intelligence. She received funding from renowned programs\, including the NSFC\, National Key R&D Young Scientist Project\, Huawei\, China Mobile\, etc. She authored over 30 high-quality SCI papers\, holds more than 80 invention patents\, and contributed to 8 industry standards. She was recognized with honors such as the China Association for Science and Technology Special Award and the Beijing Science and Technology Award. Her recent research focuses on edge intelligence\, ML/AI for PHY\, and millimeter-wave technologies\, etc. \nOrganisers\nProf. Kaibin Huang & Prof. Xianhao Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome! 
URL:https://ece.hku.hk/events/20250120-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/01/1280-4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T150000
DTEND;TZID=Asia/Hong_Kong:20241218T160000
DTSTAMP:20260510T025208
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T103000
DTEND;TZID=Asia/Hong_Kong:20241218T113000
DTSTAMP:20260510T025208
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241209T140000
DTEND;TZID=Asia/Hong_Kong:20241209T150000
DTSTAMP:20260510T025208
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241205T150000
DTEND;TZID=Asia/Hong_Kong:20241205T160000
DTSTAMP:20260510T025208
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241205T110000
DTEND;TZID=Asia/Hong_Kong:20241205T120000
DTSTAMP:20260510T025208
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
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20241026
DTEND;VALUE=DATE:20241027
DTSTAMP:20260510T025208
CREATED:20241023T073655Z
LAST-MODIFIED:20250114T033813Z
UID:19332-1729900800-1729987199@ece.hku.hk
SUMMARY:HKU InfoDay 2024 x EEE Showroom
DESCRIPTION:HKU Information Day 2024 will be held on October 26. 2024 (Saturday) 2024. Welcome to visit our laboratory and research projects in Chow Yei Ching Building. See you there! \n香港大學本科入學資訊日將於在2024年10月26日(星期六）舉行。歡迎大家來周亦卿樓參觀我們的實驗室和有趣的研究項目。到時見！ \n \nDate: 26 October 2024 (Saturday)\nTime: 9:00 am – 5:30 pm \nShowroom (1): Precision Biosensing & Biophysics Laboratory\nLocation: Room LG303\, LG3/F\, Chow Yei Ching Building\, HKU \nShowroom (2): Light and Electrical Power from Organics / Polymers\nLocation: Room 603\, 6/F\, Chow Yei Ching Building\, HKU
URL:https://ece.hku.hk/events/20241023-1/
CATEGORIES:Highlights
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END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231028
DTEND;VALUE=DATE:20231029
DTSTAMP:20260510T025208
CREATED:20231018T072519Z
LAST-MODIFIED:20250114T080205Z
UID:17741-1698451200-1698537599@ece.hku.hk
SUMMARY:HKU Information Day 2023
DESCRIPTION:Main Exhibition\nLocation: 4/F Podium\, Haking Wong Building\, The University of Hong Kong \nCome and visit our demo and lab: \n\n[Engineering Demo zone] G/F Foyer\, Composite Building\, The University of Hong Kong\n[EEE demo zone] 603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong\n[Smart Power Grid Lab] LG-201\, LG2/F\, Chow Yei Ching Building\, The University of Hong Kong\n[Semiconductor Lighting and Display Lab] 712\, 7/F\, Chow Yei Ching Building\, The University of Hong Kong\n\n  \nPlease refer to this link for the admission talk schedule:\nhttps://engg.hku.hk/News-Events/Details/id/7844 \nMore information:\nhttps://www.infoday.hku.hk/ \n  \nJoin the Information Day 2023 and register now:\nhttps://admissions.hku.hk/events/hku-information-day-ug-admissions-2023
URL:https://ece.hku.hk/events/hku-information-day-2023/
CATEGORIES:Highlights
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END:VCALENDAR