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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260410T100000
DTEND;TZID=Asia/Hong_Kong:20260410T110000
DTSTAMP:20260511T191842
CREATED:20260402T035329Z
LAST-MODIFIED:20260402T035329Z
UID:115545-1775815200-1775818800@ece.hku.hk
SUMMARY:RPG Seminar – Efficient AI for Neural Signal Decoding in Healthcare Applications
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/93628966280 \nAbstract\nRecent advances in artificial intelligence have brought new momentum to healthcare driven by neural signal decoding. This seminar presents three lines of our research on AI-based neural signal analysis across diverse application domains\, including wearable EEG-based epileptic seizure detection\, emotion recognition in SSVEP-based brain–computer interfaces (BCIs)\, and multimodal neuroimaging-based tinnitus diagnosis. First\, for real-time epileptic seizure detection on resource-constrained wearable devices\, we propose a multi-scale LBP-based hyperdimensional computing framework that captures seizure-related temporal dynamics with a compact model size\, strong few-shot learning capability\, and improved interpretability. Second\, to enhance emotion-aware interaction in SSVEP-based BCIs\, we develop a valence-arousal disentangled representation learning method that separates core emotional factors\, extracts global affective features\, and improves cross-subject generalization. Third\, for objective tinnitus diagnosis\, we introduce TinnitusLLM\, a multimodal large language model that integrates EEG and fMRI through neuro-inspired positional encoding\, multimodal autoregressive pretraining\, and subject-invariant cross-modal fine-tuning. Across these studies\, our common goal is to build efficient\, interpretable\, and clinically meaningful learning frameworks for robust neural decoding in real-world healthcare and human–machine interaction scenarios. \nSpeaker\nMr Yipeng Du\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYipeng Du is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at The University of Hong Kong. He received the B.E. degree in Communication Engineering from the University of Science and Technology Beijing and the M.E. degree in Signal and Information Processing from Peking University. His research focuses on developing deep learning methods for neural signal processing in healthcare\, particularly for disease diagnosis\, monitoring\, and brain–computer interface applications. \nOrganiser\nProf Edith C.H. Ngai \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260410/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260413T110000
DTEND;TZID=Asia/Hong_Kong:20260413T123000
DTSTAMP:20260511T191842
CREATED:20260410T042404Z
LAST-MODIFIED:20260410T044306Z
UID:115573-1776078000-1776083400@ece.hku.hk
SUMMARY:Seminar on Mechanism Design for the Evolving Landscape of Sustainable Power Systems
DESCRIPTION:Abstract\nModern power systems are undergoing rapid transformation driven by the integration of emerging technologies\, diverse participants\, and innovative business models. These changes offer unprecedented opportunities to enhance system reliability within existing infrastructure\, reduce electricity costs\, and accelerate renewable energy adoption. However\, they also introduce complex market interactions that require rigorous analytical frameworks to ensure efficiency\, fairness\, and sustainability. \nIn this talk\, the speaker will discuss our recent research on leveraging game-theoretic modelling to analyse and design mechanisms for these evolving power systems. His studies examine the impacts of coincident peak-shaving programs and energy storage participation in wholesale electricity markets through competitive equilibrium models. He also explores optimal pricing strategies for voluntary renewable energy contracts that promote large-scale renewable deployment. Together\, these results underscore the importance of a principled understanding of emerging market structures and provide guidance for designing future policies that balance economic efficiency\, reliability\, and sustainability in power systems. \nSpeaker\nProf. Bolun XU\nAssistant Professor\,\nColumbia University \nSpeaker’s Biography\nProf. Bolun XU is an Assistant Professor in Earth and Environmental Engineering at Columbia University\, with an affiliated appointment in Electrical Engineering. He received his PhD from the University of Washington and his M.S. from ETH Zurich\, both in Electrical Engineering. He also received dual bachelor degrees from Shanghai Jiaotong University and University of Michigan Ann Arbor in Electrical and Computer Engineering. Before joining Columbia\, he was a Postdoctoral Fellow at the MIT Energy Initiative. His research focuses on the design and optimisation of sustainable energy and power systems and the integration of emerging technologies. He is a recipient of the NSF CAREER Award\, the Outstanding Young Investigator Award from the IISE Energy Systems Division\, and the Early Career Award from the INFORMS Energy\, Natural Resources\, and Environment (ENRE) Section. \nOrganiser\nProf. Yi WANG\nDepartment of Electrical and Computer Engineering\,\nThe University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20260413-1/
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:20260415T153000
DTEND;TZID=Asia/Hong_Kong:20260415T163000
DTSTAMP:20260511T191842
CREATED:20260401T023508Z
LAST-MODIFIED:20260401T023508Z
UID:115523-1776267000-1776270600@ece.hku.hk
SUMMARY:RPG Seminar – Doppler LiDAR Motion Planning for Highly-Dynamic Environments
DESCRIPTION:Zoom Link:\nhttps://us05web.zoom.us/j/83514440427?pwd=4Lao2gGnkpLnw510JJXc0mXIaXCmJ5.1 \nAbstract\nExisting motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To address this limitation\, we propose integrating motion planners with Doppler LiDARs which provide not only ranging measurements but also instantaneous point velocities. However\, this integration is nontrivial due to the dual requirements of high accuracy and high frequency. To this end\, we introduce Doppler Planning Network (DPNet)\, which tracks and reacts to rapid obstacles using Doppler model-based learning. Particularly\, we first propose a Doppler Kalman neural network (D-KalmanNet) to track the future states of obstacles under partially observable Gaussian state space model. We then leverage the estimated motions to construct a Doppler-tuned model predictive control (DT-MPC) framework for ego-motion planning\, enabling runtime auto-tuning of the controller parameters. These two model-based learners allow DPNet to maintain lightweight while learning fast environmental changes using minimum data\, and achieve both high frequency and high accuracy in tracking and planning. Experiments on both high-fidelity simulator and real-world datasets demonstrate the superiority of DPNet over extensive benchmark schemes. \nSpeaker\nMr Wei Zuo\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nWei Zuo received his bachelor’s degree from Beijing Institute of Technology (BIT) in 2024\, majored in Automation. He is currently an M.Phil. candidate at the Department of Electrical and Electronic Engineering\, the University of Hong Kong\, under the supervision of Prof. Yik-Chung Wu. His current research interests include robot perception and motion planning . \nOrganiser\nProf. Yik-Chung Wu \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260415/
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:20260416T100000
DTEND;TZID=Asia/Hong_Kong:20260416T110000
DTSTAMP:20260511T191842
CREATED:20260323T025207Z
LAST-MODIFIED:20260323T025207Z
UID:115342-1776333600-1776337200@ece.hku.hk
SUMMARY:RPG Seminar – Evolving LEO Satellite Constellations into Multi-Service Platforms
DESCRIPTION:Abstract\nLow Earth orbit (LEO) satellite constellations offer global communication capabilities\, yet fundamental system challenges restrict their full utility. Specifically\, under increasing user demands\, these networks face difficulties in optimizing internal traffic distribution and efficiently delivering advanced content services. Furthermore\, seamlessly integrating space-based infrastructure with terrestrial systems to ensure resilient operations remains an unresolved issue. Solving these challenges is required to transition LEO constellations from basic connectivity relays into integrated service platforms. \nThis presentation explores optimization and extension strategies for evolving LEO satellite networks. Focusing first on the internal constellation network\, we begin with Matchmaker to address traffic distribution challenges through effective load balancing to enhance network performance. To ensure quality of service (QoS) in large-scale constellations\, an efficient heuristic algorithm that minimizes maximum satellite utilization and handovers is proposed. With this optimized network foundation\, we elevate the constellation’s capabilities through SpaceCache+\, transforming it into a space-based content delivery network (CDN). SpaceCache+ deploys cache-equipped satellites and optimizes cache replacement by managing dynamic coverage and regional preferences through zero-shot meta-learning and spatial-temporal mixing. Having expanded the constellation’s internal capacity\, we finally project its utility outward to assist terrestrial infrastructure via SpaceHelper. This work integrates satellite resources with terrestrial software-defined wide area networks (SD-WANs) to construct a resilient out-of-band control plane\, ensuring rapid reconnection during terrestrial link failures. Together\, these works illustrate a continuous system evolution\, advancing LEO networks from basic Internet access to comprehensive space-based multi-service platforms. \nSpeaker\nMr Dou Songshi\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nSongshi Dou received B.E. degree from North China Electric Power University\, Beijing\, China\, in 2019\, and M.E. degree from Beijing Institute of Technology\, Beijing\, China\, in 2022. He is currently pursuing Ph.D. degree at The University of Hong Kong\, Hong Kong SAR\, China. His research interests cover satellite Internet\, software-defined networking\, and traffic engineering. \nOrganiser\nProf. Lawrence K. Yeung\nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260416/
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:20260416T110000
DTEND;TZID=Asia/Hong_Kong:20260416T120000
DTSTAMP:20260511T191842
CREATED:20260413T041830Z
LAST-MODIFIED:20260413T042149Z
UID:115637-1776337200-1776340800@ece.hku.hk
SUMMARY:Seminar on Machine Learning for Integrated Sensing and Communication
DESCRIPTION:Abstract\nIntegrated sensing and communication (ISAC) is a key technology for the sixth-generation (6G) wireless networks\, where the same spectral and hardware resources are used for both communication and environmental sensing. Many optimization problems in ISAC require accurate sensing and communication channel models\, which are often difficult to obtain. Machine learning (ML) is a powerful tool for solving ISAC problems by enabling data-driven solutions that can bypass the reliance on explicit models. This talk will explore how ML techniques can improve ISAC performance beyond traditional optimisation approaches. Two case studies will be discussed: sensing-assisted predictive beamforming and cooperative sensing through ML. These examples will demonstrate the potential of ML to enable end-to-end signal processing for ISAC in 6G wireless networks. \nSpeaker\nProf. Vincent WONG\nProfessor\,\nDepartment of Electrical and Computer Engineering\,\nUniversity of British Columbia\, Vancouver\, Canada \nSpeaker’s Biography\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\, optimisation\, 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\, Canadian Academy of Engineering\, and the Engineering Institute of Canada. \nOrganisers\nProf. Kaibin HUANG & Prof. Xianhao CHEN\nDepartment of Electrical and Computer Engineering\,\nThe University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20260416-2/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2026/04/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260417T090000
DTEND;TZID=Asia/Hong_Kong:20260417T100000
DTSTAMP:20260511T191842
CREATED:20260402T024124Z
LAST-MODIFIED:20260402T024124Z
UID:115542-1776416400-1776420000@ece.hku.hk
SUMMARY:RPG Seminar – System Design for Wide-field Miniature Fluorescence Microscope
DESCRIPTION:Zoom Link:\nhttps://us05web.zoom.us/j/89432980495?pwd=LSDClCuSVwd9fL5oZaI532byF2aC1a.1 \nAbstract\nMiniature fluorescence microscope\, or miniscope\, is a compact fluorescence imaging system that integrates the excitation source\, optical filters\, imaging optics\, and image sensor into a small platform.  Among various technical approaches\, one-photon widefield miniscopes are especially attractive because of their relatively simple architecture\, strong engineering feasibility\, and compatibility with two-dimensional superficial imaging tasks. However\, existing miniscope systems still face important challenges\, particularly in balancing a large field of view with high spatial resolution\, maintaining image quality across the full field\, and achieving compact system integration. \nThis seminar focuses on the optical design of one-photon widefield miniature fluorescence microscopes for two-dimensional superficial imaging\, with the main emphasis placed on a single-channel system. The study investigates the key design considerations of excitation and emission light separation\, imaging optics configuration\, aberration control\, and system compactness\, aiming to achieve high-resolution fluorescence imaging over a relatively large field of view within a miniature platform. On this basis\, scalable multi-channel architectures are briefly considered as extensions to examine the feasibility of multi-channel integration in miniature fluorescence imaging systems. The work provides a system-oriented design framework for one-photon widefield miniscopes and highlights practical strategies for improving imaging performance\, structural integration\, and architectural scalability. \nSpeaker\nMr Wentao Ye\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nWentao Ye is an MPhil student in the Department of Electrical and Computer Engineering at The University of Hong Kong\, supervised by Prof. Feifei Wang. He received his B.S. degree in Applied physics from Tianjin University in 2024. Her research interests include miniature fluorescence microscope and biomedical image processing. \nOrganiser\nProf. Feifei Wang \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260417/
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:20260417T143000
DTEND;TZID=Asia/Hong_Kong:20260417T153000
DTSTAMP:20260511T191842
CREATED:20260413T015147Z
LAST-MODIFIED:20260413T015147Z
UID:115631-1776436200-1776439800@ece.hku.hk
SUMMARY:RPG Seminar – A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/99219579381?pwd=IjEXStaAdwdBhtSyoZ0aYbLavr0zhX.1 \nAbstract\nDeep learning (DL)-based methods have achieved remarkable success in continuous wireless resource allocation\, yet its application to problems involving discrete variables remains hindered by the zero-gradient issue\, complex constraint enforcement\, and the lack of non-same-parameter-same-decision (non-SPSD) guarantees. To address these challenges\, this seminar presents a novel DL framework that leverages probabilistic modeling of the discrete support set to enable efficient\, end-to-end optimization. The speaker will further demonstrate the efficacy of this framework through two challenging mixed-discrete wireless resource allocation applications: (a) joint user association and beamforming in cell-free systems\, and (b) joint antenna positioning and beamforming in movable antenna-aided systems. The results demonstrate the superior performance of the proposed framework compared to the  state-of-the-art optimization algorithms and learning-based methods. \nSpeaker\nMr. Yikun WANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYikun Wang received the B.E. degree in Communication Engineering from Beihang University. He is currently pursuing the M.Phil. degree in the Department of Electrical and Computer Engineering at The University of Hong Kong\, under the supervision of Prof. Yik-Chung Wu. His research interests include deep learning-based optimization for wireless systems. \nOrganiser\nProf. Yik-Chung WU \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260417-2/
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:20260421T110000
DTEND;TZID=Asia/Hong_Kong:20260421T120000
DTSTAMP:20260511T191842
CREATED:20260326T042150Z
LAST-MODIFIED:20260326T090341Z
UID:115419-1776769200-1776772800@ece.hku.hk
SUMMARY:RPG Seminar – Fast customization of color centers in single nanodiamonds and its applications
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/91528535994?pwd=pagmffbltruagBNPmFkEWo5sxaFJrN.1 \nAbstract\nDeterministic control of nitrogen–vacancy (NV) centers in nanodiamonds (NDs) is critical for emerging applications in quantum photonics and high-density optical data storage. Conventional approaches based on random irradiation and thermal annealing often lead to nondeterministic NV formation and lattice damage\, making it difficult to precisely control the optical properties of the color centers and resulting in low fabrication yields. In this seminar\, we present a rapid and targeted approach for customizing NV centers in pre-selected single nanodiamonds using a standard transmission electron microscope (TEM). A brief 1-second electron beam exposure enables NV formation without post-annealing while minimizing lattice damage. The resulting nanodiamonds exhibit stable optical and spin properties. Furthermore\, controlled TEM exposure can quench NV photoluminescence within tens of seconds\, enabling a fast write–erase process. Leveraging this capability\, we demonstrate a flexible nanodiamond-based optical data storage platform. This accessible and highly targeted method provides a powerful route for on-demand NV center engineering and opens new opportunities for quantum technologies and nanoscale photonic data storage. \nSpeaker\nMiss Jiaqi Li\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nJiaqi Li received her BEng degree in Physics from South China Normal University in 2022. She is currently pursuing a PhD in the Department of Electrical and Computer Engineering at the University of Hong Kong. Her research focuses on the fabrication\, characterization\, and applications of nitrogen-vacancy (NV) centers in diamond materials\, particularly in nanodiamonds. \nOrganiser\nProf. Zhiqin Chu \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260421/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260424T110000
DTEND;TZID=Asia/Hong_Kong:20260424T120000
DTSTAMP:20260511T191842
CREATED:20260410T024359Z
LAST-MODIFIED:20260410T024359Z
UID:115568-1777028400-1777032000@ece.hku.hk
SUMMARY:RPG Seminar – From 2D to 3D: Enhancing Ultrasound Resolution through Null Subtraction Beamforming
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/99658715385?pwd=NaDaq5EIkqa3RYU4pDDYxbz5stbh45.1 \nAbstract\nHigh-frame-rate ultrasound imaging is a cornerstone of modern medical diagnostics and structural monitoring\, yet it remains bound by the classical trade-off between spatial resolution and acquisition speed. While conventional delay-and-sum (DAS) beamforming is the industry standard\, its resolution is fundamentally limited by the transducer’s diffraction pattern. This seminar introduces Null Subtraction Imaging (NSI)\, a nonlinear beamforming strategy designed to surpass these diffraction limits. We begin by establishing the principles of 2D NSI\, where thee apodization windows are used to synthesize a “null” beampattern that effectively sharpens the mainlobe. Building on this foundation\, the talk focuses on the extension of this technique into 3D NSI. We discuss the unique challenges of volumetric imaging\, including the complexity of 2D aperture design and the computational demands of matrix arrays. By leveraging specialized apodization masks and hardware-efficient sampling strategies\, 3D NSI achieves improvements in both azimuthal and elevational resolution\, as well as contrast ratio\, over DAS. The presentation will detail the theoretical framework\, simulation results\, and experimental validation\, demonstrating a scalable path toward high-resolution\, real-time 4D ultrasound imaging. \nSpeaker\nMr Bingze Dai\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nBingze Dai received his M.S. in Electrical and Computer Engineering at University of Illinois\, Urbana-Champaign in 2023\, and B.S. in Electronic and Information Engineering at Beijing Institute of Technology in 2019. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Hong Kong\, with a research focus on ultrasound imaging. \nOrganiser\nProf. Wei-Ning Lee \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260424/
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:20260424T140000
DTEND;TZID=Asia/Hong_Kong:20260424T150000
DTSTAMP:20260511T191842
CREATED:20260414T022321Z
LAST-MODIFIED:20260414T022414Z
UID:115683-1777039200-1777042800@ece.hku.hk
SUMMARY:RPG Seminar – Multi-Dimensional Nano-Printing of Colloidal Quantum Dots for Infrared Optoelectronics
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/92581464035?pwd=APBvw8rHe96aZfdqA9bhiKatjwmjXe.1 \nAbstract\nAdditive manufacturing enables the bottom-up fabrication of tailored and functional optoelectronic devices. However\, realizing printed (opto)electronics with micro- and nanoscale features and multi-dimensional structures remains challenging.\nHere\, we developed electrohydrodynamic (EHD) printing as the platform for the in-situ deposition of sub-micron-resolution colloidal quantum dot (QD) inks from quasi-2D monolayers to 2D lines and films\, and to quasi-3D walls.\nBy optimizing printing parameters\, such as the amplitude and offset of the applied waveform\, as well as the printing speed\, uniform and continuous 2D QD lines with linewidths as small as 300 nm can be obtained without post-processing.\nThen we performed solvent engineering on the QD ink\, enabling printed QDs to form closely packed quasi-2D monolayers and even short-range-ordered hexagonal superlattices. Besides\, by stacking multiple printed 2D lines\, our printer enables the fabrication of quasi-3D QD walls with aspect ratios ranging from 0.25 to 2.\nFinally\, we demonstrate QD patterning with a resolution exceeding 10\,000 dots per inch (dpi) and tunable thickness. The multi-dimensional EHD-printed QD arrays can serve as color-conversion layers for micro-LEDs (thicknesses over 1 μm) and also hold promise as a new solution for the patterning of QD-LED emissive layers (thicknesses less than 100 nm). \nSpeaker\nMr. Zeqi WANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZeqi Wang received his BEng in Optoelectronic Information Science and Engineering from Southern University of Science and Technology in 2024. He is currently pursuing an MPhil degree with Prof. Zhao in the Department of Electrical and Computer Engineering\, with a research focus on electrohydrodynamic printing of infrared colloidal quantum dots. \nOrganiser\nProf. Leo Tianshuo ZHAO \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260424-2/
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:20260424T150000
DTEND;TZID=Asia/Hong_Kong:20260424T160000
DTSTAMP:20260511T191842
CREATED:20260420T022232Z
LAST-MODIFIED:20260420T022232Z
UID:115713-1777042800-1777046400@ece.hku.hk
SUMMARY:RPG Seminar – A BO-based Method for Parameter Tuning in Voltage Controlled DERs to Improve Flexibility Area at TSO-DSO Interface
DESCRIPTION:Zoom Link:\nhttps://us05web.zoom.us/j/82211644170?pwd=MUs6qBHh0EZskKbZOsacWOHAGHK4rQ.1 \nAbstract\nThe rapid proliferation of aggregated flexible resources in active distribution systems (ADSs) calls for accurate estimation of the flexibility area (FA) at the transmission system operator–distribution system operator (TSO–DSO) interface to improve operational coordination. Meanwhile\, voltage control plays a critical role in maintaining both economic and secure grid operation. However\, the relationship between voltage control parameters and the FA metric is highly nonlinear and analytically intractable\, as inverter droop coefficients and other control parameters interact with network constraints and distributed energy resource (DER) operating conditions in a coupled manner. \nTo address this challenge\, this paper proposes a Bayesian optimization (BO)-based framework that systematically tunes voltage support parameters to maximize the FA potential. The main contributions are threefold: (1) a cross-layer optimization paradigm that links inverter-level droop control with systemlevel FA characteristics\, revealing how voltage control curves shape interface power flow dynamics; (2) a reformulated FA estimation process in which the Interval-Constrained Power Flow (ICPF) model\, embedded with droop-control operations\, is cast as a Bayesian black-box problem solved efficiently via Gaussian process surrogates with a tile-coding kernel; and (3) a composite performance index\, namely the Secure Area–Cost Flexibility (SeCoF)\, which jointly quantifies the trade-off between flexibility enhancement and operational cost. Case studies on modified IEEE 13-bus and IEEE 37-bus distribution systems demonstrate the effectiveness\, robustness\, and scalability of the proposed method in improving both technical flexibility and economic performance under realistic operational constraints. \nSpeaker\nMiss Zihui HONG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZihui Hong received his BEng in Electrical and Electronic Engineering from Huazhong University of Science and Technology in 2024. She is currently pursuing an MPhil degree with Prof. Hou in the Department of Electrical and Computer Engineering\, the University of Hong Kong with a research focus on Bayesian Methods for Estimation and Optimization of Flexibility Region in Power Systems. \nOrganiser\nProf. Yunhe HOU \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260424-3/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260425T090000
DTEND;TZID=Asia/Hong_Kong:20260425T173000
DTSTAMP:20260511T191842
CREATED:20260310T093703Z
LAST-MODIFIED:20260326T023321Z
UID:115286-1777107600-1777138200@ece.hku.hk
SUMMARY:HKUECE: RoboLeague 2026 cum RoboCup Junior China Open (Hong Kong)
DESCRIPTION:*** Application deadline is on April 2\, 2026 (Thursday) at 8:00 pm ***\n\n🚀 Join the Excitement of Robotics!\n🌟 Please stay tuned to event-day announcements for any schedule adjustments or important updates! 🤩 \nCompetition Details\n📅 Date: April 25\, 2026 (Saturday)\n🕒 Time: 9:00 am – 5:30 pm\n📍Venue: The University of Hong Kong \nCompetition Description\nEngage your autonomous robots in thrilling challenges: \n– Soccer League (Open) （足球公開租）\n– Soccer League (Lightweight) （足球輕量組）\n– Soccer League (Standard Platform) （足球標準平台組）\n– Rescue League (Line) （搜救循線組）\n– Rescue League (Maze) （搜救迷宮組）\n– Mini-rescue League （迷你救援組） \nWho Can Join?\nTarget Audience: Secondary School Students Team\nFormation: 2-4 students ( any Forms ) per team from the same school \nKey Dates\n*Briefing Session: \n📅 Date: March 28\, 2026 (Saturday) @1:00 pm\n📍Venue: Online via Zoom \n*Application Deadline: \n📅 Date: April 2\, 2026 (Thursday) @8:00 pm \nRegistration & More Details\nhttps://linktr.ee/hkuecerl \nEnquiries\nPlease contact us via WhatsApp at 5793 9462 or email at hkuecerl@gmail.com\, using the subject line “HKUECERL2026 Enquiry”.
URL:https://ece.hku.hk/events/rl2026/
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260427T103000
DTEND;TZID=Asia/Hong_Kong:20260427T113000
DTSTAMP:20260511T191842
CREATED:20260422T071526Z
LAST-MODIFIED:20260422T071526Z
UID:115748-1777285800-1777289400@ece.hku.hk
SUMMARY:RPG Seminar – Toward Trustworthy Network Intrusion Detection: From Representation Learning to Multi-Agent Orchestration
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/94349548162?pwd=rk4r0ND8iT1JzXjGsg293RmHKPGrjg.1 \nAbstract\nModern network intrusion detection systems face critical challenges in open and dynamic environments\, including concept drift as threat patterns evolve\, label noise as annotations are inherently unreliable\, and limited interpretability as detection decisions are difficult to understand and trust. This seminar presents our recent progress toward a trustworthy intrusion detection paradigm. We propose a representation enhancement framework that learns drift-aware and noise-robust feature embeddings\, enabling detectors to sustain high accuracy under non-stationary and imperfectly labeled conditions. We further introduce an LLM-based hierarchical multi-agent system that brings semantic reasoning into intrusion detection\, delivering both strong detection performance and human-interpretable explanations. These works chart a path from robust representation learning to next-generation agentic inference\, advancing network intrusion detection toward greater robustness\, adaptivity\, and trustworthiness. \nSpeaker\nMr Shuo YANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nShuo Yang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at The University of Hong Kong\, under the supervision of Prof. Edith C. H. Ngai. His current research interests include Network Security\, Trustworthy AI\, Data Mining and LLM Agent. \nOrganiser\nProf Edith C. H. NGAI \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260427-2/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260427T143000
DTEND;TZID=Asia/Hong_Kong:20260427T153000
DTSTAMP:20260511T191842
CREATED:20260413T092309Z
LAST-MODIFIED:20260413T092309Z
UID:115657-1777300200-1777303800@ece.hku.hk
SUMMARY:RPG Seminar – Mobile Reasoning-as-a-Service via Distributed LLM Inference-Time Scaling
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/6589092185?pwd=19EpQ4AqzgRRwQzym8vF0aaKui1J2b.1 \nAbstract\nInference-time scaling has emerged as an effective approach for enhancing the capabilities of Large Language Models (LLMs)\, addressing the growing demand for stronger reasoning without increasing model size. This novel form of LLM scaling comprises two representative approaches: explicit reasoning\, which generates intermediate chain-of-thought tokens during an explicit thinking phase\, and implicit reasoning\, which iteratively updates hidden states in the latent space without producing explicit outputs. Despite their effectiveness\, both paradigms incur substantial computational and memory overhead\, raising challenges for deployment on resource-constrained edge devices. To address these issues\, we propose a Mobile Reasoning-as-a-Service framework that treats reasoning as a computational service accessible to edge devices over wireless networks. Focusing on implicit reasoning\, we leverage its recursive structure to partition hidden-state updates between edge devices and servers\, enabling cooperative inference that allows devices to access additional cloud computation on demand. To handle dynamic wireless conditions and optimize long-term performance\, we formulate a joint computation and communication scheduling problem and solve it using a semantic Mixture-of-Experts (MoE)-based Soft Actor-Critic (SAC) algorithm to address heterogeneity in wireless conditions and task demands. Ultimately\, this work validates distributed inference-time scaling through semantic-aware collaborative reasoning services\, offering a scalable and efficient paradigm for deploying advanced LLM reasoning at the mobile edge \nSpeaker\nMr. Guanchen LIU\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nGuanchen Liu received his B.S. degree from the Harbin Institute of Technology. He is currently an MPhil candidate in the Department of Electrical and Computer Engineering at the University of Hong Kong\, with a research focus on LLM reasoning. \nOrganiser\nProf. Kaibin HUANG \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260427/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260429T160000
DTEND;TZID=Asia/Hong_Kong:20260429T170000
DTSTAMP:20260511T191842
CREATED:20260420T023103Z
LAST-MODIFIED:20260420T023132Z
UID:115717-1777478400-1777482000@ece.hku.hk
SUMMARY:RPG Seminar – Intrinsic Wavelength Tuning in InGaN/GaN MQWs Nano/MicroLEDs
DESCRIPTION:  \nAbstract\nIntrinsic wavelength tuning in InGaN/GaN multiple quantum wells (MQWs) is a critical aspect of optimizing their performance for various applications\, including display technologies and visible light communication. This project investigates the underlying mechanisms of wavelength tuning in InGaN/GaN MQWs and explores strategies to achieve precise control over the emission wavelength. Through a combination of experimental studies and theoretical modeling\, we analyze the effects of piezoelectric strain\, indium composition\, and external electric field on the emission properties of nano/microLEDs. The findings provide insights into the design and fabrication of LEDs with tuable emission characteristics\, contributing to advancements in optoelectronic device engineering. \nSpeaker\nMr. Bo LU\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nBo LU received his BSc degree in Physics from the China University of Mining and Technology in 2018 and MSc from the Southern University of Science and Technology (HIT joint program). He is currently pursuing a PhD in the Department of Electrical and Computer Engineering at the University of Hong Kong. His research focuses on the fabrication\, characterization\, and applications of long-wavelength InGaN/GaN MQWs microLED\, particularly in wavelength tuning engineering. \nOrganiser\nProfessor Anthony Hoi Wai CHOI \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260429/
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:20260505T140000
DTEND;TZID=Asia/Hong_Kong:20260505T150000
DTSTAMP:20260511T191842
CREATED:20260421T023945Z
LAST-MODIFIED:20260421T023945Z
UID:115744-1777989600-1777993200@ece.hku.hk
SUMMARY:RPG Seminar – Accuracy-Oriented Resource Scheduling for Satellite-Air-Ground Sensing\, Computing\, and Communication Systems
DESCRIPTION:  \nAbstract\nWith the evolution of 6G mobile communications\, achieving 99.999% seamless global coverage has become a key requirement. Traditional terrestrial networks still suffer from coverage blind spots in scenarios such as emergency rescue and vast ocean areas. Therefore\, building a space–air–ground integrated network by combining satellite\, aerial\, and terrestrial systems has become an important trend. This seminar focuses on mission-driven target recognition within the integrated sensing\, communication\, and computing (ISCC) framework\, emphasizing efficient end-to-end system design under physical constraints. It analyzes the theoretical lower bounds of multiclass classification error probability\, including discrimination gain and feature distribution variance\, and discusses multi-objective trade-offs and optimization frameworks. The seminar also highlights future research on user-oriented intelligent scheduling for space–air–ground collaborative resources\, with the goal of reducing end-to-end latency\, improving resource allocation efficiency\, and enhancing overall system performance. \nSpeaker\nMr Zhiyuan XU\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nXu Zhiyuan received the B.Sc. degree from Sun Yat-sen University\, Guangzhou\, China\, in 2023. He is currently working toward the M.Phil. degree with The University of Hong Kong\, Hong Kong. His research interests include 6G communications. \nOrganiser\nProf. Kaibin HUANG \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260505/
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:20260508T143000
DTEND;TZID=Asia/Hong_Kong:20260508T153000
DTSTAMP:20260511T191842
CREATED:20260430T030808Z
LAST-MODIFIED:20260430T031009Z
UID:115806-1778250600-1778254200@ece.hku.hk
SUMMARY:RPG Seminar – Novel Two-Dimensional (2D) Memory Devices: From Material Innovation to Functional Integration
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/92285676627?pwd=oaba8ue6daTbmrDBhxaIhYsykCedbR.1 \nAbstract\nDuring the period of the Internet of Things (IoT) and big data\, data capacity is growing exponentially\, the delay and loss caused by data transmission make the traditional von Neumann computing architecture urgently need to be overturned and restructured. This has led to a growing demand for emerging memory devices\, posing significant challenges to conventional silicon-based memory technologies. In recent years\, two-dimensional (2D) materials leveraging their unique physical properties\, ultra-thin thickness and no dangling bonds have been wide-ranging used in the fabrication of various electronic and optoelectronic memory devices. In this seminar\, we first briefly introduce several mainstream types of 2D memory devices developed in recent years and their working mechanisms. We then propose a 2D infrared-sensing memory device (ISMD) based on the Se0.3Te0.7/CuInP2S6 (CIPS) heterostructure\, where Se0.3Te0.7 serves as the channel and CIPS functions as the ferroelectric auxiliary layer. The coupling between interfacial defect trapping and ferroelectric polarization endows the device with non-volatile multi-bit memory capability programmable by electrical pulses. Meanwhile\, the device exhibits a transient infrared response at 1550 nm\, with its responsivity negatively correlated to the channel conductance. By integrating sensing\, memory\, and computing in a single device\, this work broadens the research scope of 2D memory devices. \nSpeaker\nMr. Xuyang ZHENG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nXuyang Zheng is an MPhil student in the Department of Electrical and Computer Engineering at The University of Hong Kong\, supervised by Prof. Can Li. He received his B.S. degree in Functional Materials from South China University of Technology (SCUT) in 2024. His research interests include 2D memory devices for neuromorphic computing. \nOrganiser\nProf. Can LI \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260508/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260508T160000
DTEND;TZID=Asia/Hong_Kong:20260508T170000
DTSTAMP:20260511T191842
CREATED:20260504T020614Z
LAST-MODIFIED:20260504T020614Z
UID:115819-1778256000-1778259600@ece.hku.hk
SUMMARY:RPG Seminar – Day–Night Mechanism-Aware Causal Modeling for Wind Power Forecasting: A Physics-Guided NLSEM Framework
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/95263844640 \nAbstract\nWind power forecasting remains highly challenging due to the strong nonlinearity of atmospheric dynamics\,pronounced diurnal regime differences\, and substantial uncertainties in multi-source meteorological data. Conventional black-box machine-learning models mainly rely on observational correlations\, often neglecting physical constraints and causal mechanisms\, which leads to limited interpretability and poor robustness under distribution shifts. In this seminar\, we proposes a unified forecasting framework that integrates physics-constrained data construction\, multi-site causal structure learning\, and a global nonlinear structural equation model (NLSEM). The framework combines full-variable Granger causality networks with PCMCI+ to identify distinct day and night-time causal directed acyclic graphs (DAGs). Within the NLSEM\, physical monotonicity\, environmental invariance\, and counterfactual-consistency regularization are explicitly enforced. The resulting model supports causal inference through dointervention analysis\, ATE/CATE estimation\, and counterfactual reasoning. Experiments conducted on three coastal wind farms demonstrate consistent performance improvements over strong machine-learning baselines\, while revealing physically meaningful causal drivers of wind-power generation. \nSpeaker\nMr. Yuxuan WANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYuxuan Wang received the B.S. degree in New Energy Science and Engineering from Huazhong University of Science and Technology\, and the M.S. degree in electrical engineering from University of Leeds. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His current research interests include wind power forecasting\, causal inference\, and nonlinear modeling for renewable energy systems. \nOrganiser\nProf. Yunhe HOU \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260508-2/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260512T160000
DTEND;TZID=Asia/Hong_Kong:20260512T170000
DTSTAMP:20260511T191842
CREATED:20260511T041652Z
LAST-MODIFIED:20260511T041652Z
UID:115894-1778601600-1778605200@ece.hku.hk
SUMMARY:RPG Seminar – HOPE for Quantitative Photoacoustic Microscopy of Lipids: From Acquisition to Analysis
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/7489542129?pwd=EgQ5WhPS1YJcx9cbWlXa2HMbqlxJxC.1&omn=96943285907 \nAbstract\nPhotoacoustic microscopy (PAM) in the 1.7-μm absorption window has emerged as a promising modality for label-free lipid imaging due to the strong overtone absorption of C—H bonds at this wavelength. However\, quantitative lipid imaging is constrained by the performance of existing nanosecond laser sources and depth-dependent resolution degradation caused by optical scattering. In this seminar\, we present a full-stack optical-resolution PAM platform for the quantitative imaging and analysis of lipids in biological tissues. The key constituent of this platform is a novel 1725-nm hybrid optical parametric oscillator emitter (HOPE) source tailored for PAM applications. The laser source provides superior spectral energy density\, narrow bandwidth\, and high optical signal-to-noise ratio\, allowing for high-sensitivity mapping of lipids in tissues. Using this system\, we demonstrate clinically relevant differentiation of hepatic steatosis levels in ex vivo human liver specimen\, with demonstrated potential as a diagnostic tool for metabolic dysfunction-associated steatotic liver disease (MASLD). We further develop a physics-informed deconvolution framework incorporating to address scattering-induced resolution degradation in three-dimensional PAM images. The framework achieves substantial resolution recovery of up to approximately twofolds in out-of-focus regimes and is additionally applied to the volumetric visualisation of laser-induced thermal ablation lesions in human liver tissue. Together\, these developments establish a comprehensive 1.7-μm PAM framework for high-sensitivity lipid imaging and advanced volumetric tissue characterisation. \nSpeaker\nMs Najia SHARMIN\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMs. Najia Sharmin is a PhD candidate in the Photonic Systems Research Lab at The University of Hong Kong under the Hong Kong PhD Fellowship Scheme and the HKU Presidential PhD Scholar Programme. Her research focuses on advancing photoacoustic microscopy for biomedical applications\, combining laser system design\, optical modeling\, and image processing to improve diagnostic and translational capabilities. Her PhD work is particularly centered on non-invasive\, label-free detection of lipid distributions in human liver tissues in the 1.7-µm optical window. \nOrganiser\nProf Kenneth K.Y. Wong \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260512/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260513T100000
DTEND;TZID=Asia/Hong_Kong:20260513T110000
DTSTAMP:20260511T191842
CREATED:20260507T082216Z
LAST-MODIFIED:20260507T082216Z
UID:115875-1778666400-1778670000@ece.hku.hk
SUMMARY:RPG Seminar – Towards Controllable and Cinematic Visual Content Generation
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/96029601263 \nAbstract\nThis seminar presents novel solutions to bridge the gap between creative intent and automated visual synthesis. We first address the challenge of compositional alignment in text-to-image generation. This includes T2I-CompBench(++) for systematic evaluation of multi-attribute prompts\, and GenMAC\, a multi-agent collaborative framework that enables precise semantic orchestration. Second\, we target the lack of cinematic artistry in video generation via Filmaster\, a system that injects professional camera language and rhythm into the synthesis process using real-film references. Finally\, we tackle spatio-temporal inconsistency in dynamic sequences using CineScene\, which leverages implicit 3D-aware representations to preserve scene consistency across camera trajectories. Together\, these contributions pave the way for next-generation controllable tools in digital cinematography and professional content creation. \nSpeaker\nMs Kaiyi HUANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nKaiyi Huang received her BSc degree and MSc in Electrical Engineering from the Shanghai Jiao Tong University\, and a double MSc degree from Ecole Centrale Paris (joint program). She is currently pursuing a PhD in the Department of Electrical and Computer Engineering at the University of Hong Kong. Her research focuses on the image/video generation\, film generation and world model. \nOrganiser\nProf Xihui LIU \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260513/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260514T103000
DTEND;TZID=Asia/Hong_Kong:20260514T113000
DTSTAMP:20260511T191842
CREATED:20260508T020416Z
LAST-MODIFIED:20260508T020416Z
UID:115879-1778754600-1778758200@ece.hku.hk
SUMMARY:RPG Seminar – HIMSA: A Heterogeneous In-Memory Computing and Searching Architecture for Efficient Attention-Based Models
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/99174148480?pwd=duVxaYZOJDT6MWxDh4OKOMmyo12A7A.1 \nAbstract\nThe Transformer architecture\, the foundation for modern large language models (LLMs)\, has revolutionized natural language processing and other AI domains. However\, its significant computational and memory requirements\, primarily from the matrix multiplication in the self-attention mechanism\, present major challenges for conventional hardware. While intensive research on in-memory computing (IMC) technology offers a path to overcome the memory bottleneck\, using IMC for Transformers remains challenging. This is because the dynamic matrix multiplication with frequently changing Key\, Query\, and Value matrices require frequent and costly write operations that are ill-suited for non-volatile memories (NVM) technologies like ReRAM. This work introduces HIMSA Heterogeneous In-Memory Computing and Searching Architecture\, which employs vector quantization on K and V matrices. This technique transforms the dynamic vector-matrix multiplications into static operations performed on pre-trained codebooks\, thereby eliminating the need for runtime write operations in the attention mechanism. The proposed architecture was evaluated through circuit-level simulations that account for the peripheral circuit designs\, including nearest neighbor search and the division-less Softmax operations. More importantly\, its write-free attention mechanism mitigates the concerns over limited write endurance of ReRAM devices . This work presents a promising pathway toward highly efficient NVM-based hardware acceleration for next-generation AI models. \nSpeaker\nMr Muyuan PENG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nPeng Muyuan received the B.S. degree at the University of Science and Technology of China majored in Applied Physics. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His current research interests include non-volatile memory devices\, in-memory computing and related neural network accelerations. \nOrganiser\nProfessor Can LI \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260514-2/
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:20260514T133000
DTEND;TZID=Asia/Hong_Kong:20260514T143000
DTSTAMP:20260511T191842
CREATED:20260505T021413Z
LAST-MODIFIED:20260505T021413Z
UID:115830-1778765400-1778769000@ece.hku.hk
SUMMARY:RPG Seminar – Cooperative Edge AI: From Event-triggered Inference to Efficient Model Downloading
DESCRIPTION:Zoom Link \nhttp://hku.zoom.us/j/7074144117?omn=95813783034 \nAbstract\nCooperative edge AI enables edge devices and edge servers to collaboratively execute intelligent tasks under limited computation\, storage\, energy\, and communication resources. In this talk\, we discuss two complementary research directions toward communication-efficient cooperative edge AI. First\, we introduce an event-triggered cooperative inference framework for rare-event detection in edge intelligence systems. Rare events are usually infrequent but highly critical\, while conventional edge inference systems may overlook them due to data imbalance and rigid resource allocation. To address this issue\, a dual-threshold multi-exit architecture is adopted\, allowing confident normal events to be processed locally while complex or uncertain rare events are selectively offloaded to the edge server for more accurate classification. Second\, we present an efficient AI model downloading framework based on parametric-sensitivity-aware retransmission. Instead of treating all model parameters equally\, this framework exploits the unequal importance of neural network parameters and allocates wireless retransmission resources to more sensitive model packets. In this way\, downloading latency can be reduced while inference performance is preserved. The talk concludes with a discussion of future research directions in cooperative edge AI\, highlighting open challenges and opportunities in communication-efficient inference\, adaptive model deployment\, and resource-aware edge intelligence. \nSpeaker\nMr Zhou You\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZhou You is currently pursuing a Ph.D. degree in the Department of Electrical and Electronic Engineering at The University of Hong Kong\, under the supervision of Prof. Kaibin Huang. He received his B.Eng. degree in Electrical Engineering from the University of Wisconsin–Madison\, USA\, in 2021. His research interests include wireless communications\, edge inference\, and AI model downloading. \nOrganiser\nProf. Kaibin HUANG \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260514/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260515T160000
DTEND;TZID=Asia/Hong_Kong:20260515T170000
DTSTAMP:20260511T191842
CREATED:20260511T020048Z
LAST-MODIFIED:20260511T020048Z
UID:115888-1778860800-1778864400@ece.hku.hk
SUMMARY:RPG Seminar – Unveiling the Relationship Between Cation Content and Zeta Potential of Colloids for Forming High-Quality Perovskites
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/94017530661 \nAbstract\nThere are numerous studies focusing on the crystallization dynamics of perovskite materials. However\, the change of precursor properties which can also significantly affect crystallization behavior\, is always ignored. In this seminar\, we establish a comprehensive understanding of the relationship between A-site cations content and zeta potential of precursor\, revealing its influence on perovskite formation and crystallization dynamics. Through in-situ photoluminescence (PL) and X-ray diffraction (XRD) analyses\, we demonstrate how zeta potential impacts the formation process and crystallization behavior of perovskites. Furthermore\, we explore the effects of zeta potential on the optical and electrical properties of the resulting materials. Our findings indicate that achieving a zeta potential near zero facilitates the fabrication of high-quality and additive-free perovskites\, leading to enhanced performance in perovskite solar cells (PSCs) and perovskite light-emitting diodes (PeLEDs). This work provides vital insights into tuning interfacial properties for improved perovskite optoelectronic devices. \nSpeaker\nMr. Qi XIONG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nQi Xiong received the B.S. degree in Polymer Materials and Engineering from Hainan University\, and the M.S. degree in Material Science and Engineering from South China University of Technology. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering\, Faculty of Engineering\, The University of Hong Kong. His current research interests include perovskite synthesis and blue perovskite light-emitting diodes (PeLEDs). \nOrganiser\nProf. Wallace C.H. CHOY \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260515/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260519T160000
DTEND;TZID=Asia/Hong_Kong:20260519T170000
DTSTAMP:20260511T191842
CREATED:20260420T064633Z
LAST-MODIFIED:20260420T064633Z
UID:115720-1779206400-1779210000@ece.hku.hk
SUMMARY:RPG Seminar – From Understanding to Intervention: Interpretability-Guided Methods for Improving Large Language Models
DESCRIPTION:  \nAbstract\nLarge language models have achieved impressive performance\, but improving them efficiently and reliably requires more than scaling alone. In this talk\, I present a series of works that explore how internal understanding of LLMs can be translated into practical interventions for better capability\, efficiency\, and controllability. I begin with actionable mechanistic interpretability\, introducing a unified “Locate\, Steer\, and Improve” perspective that turns model analysis into a framework for intervention. I then show how this perspective supports several concrete advances: data-free mixed-precision quantization guided by numerical and structural sensitivity\, multilingual capability enhancement through representation shifting and contrastive alignment\, personalized multi-teacher distillation that routes each prompt to its most suitable teacher\, and coarse-to-fine selective fine-tuning for mitigating catastrophic forgetting while preserving general versatility. Together\, these works reflect a common theme: interpretability is not only a tool for explaining LLMs\, but also a principled basis for designing more efficient training\, compression\, and adaptation methods. \nSpeaker\nMr Hengyuan ZHANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nHengyuan Zhang is a Ph.D. candidate at the University of Hong Kong\, supervised by Prof. Ngai Wong and Prof. Hayden Kwok-Hay So. His research focuses on the improvement of efficiency and interpretability within large language models. He aims to uncover and characterize the internal processes that govern model behavior\, with the goal of improving model speciality\, interpretability\, and reliability in real-world deployments. He has published multiple papers in leading venues such as ACL\, EMNLP\, TKDD\, and NeurIPS. \nOrganiser\nProf. Ngai WONG \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260519/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
END:VCALENDAR