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PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.15.20//NONSGML v1.0//EN
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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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TZID:Asia/Hong_Kong
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TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260505T140000
DTEND;TZID=Asia/Hong_Kong:20260505T150000
DTSTAMP:20260511T143758
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
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:20260508T143000
DTEND;TZID=Asia/Hong_Kong:20260508T153000
DTSTAMP:20260511T143758
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
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:20260508T160000
DTEND;TZID=Asia/Hong_Kong:20260508T170000
DTSTAMP:20260511T143759
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260512T160000
DTEND;TZID=Asia/Hong_Kong:20260512T170000
DTSTAMP:20260511T143759
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260513T100000
DTEND;TZID=Asia/Hong_Kong:20260513T110000
DTSTAMP:20260511T143759
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
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:20260514T103000
DTEND;TZID=Asia/Hong_Kong:20260514T113000
DTSTAMP:20260511T143759
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:20260511T143759
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260515T160000
DTEND;TZID=Asia/Hong_Kong:20260515T170000
DTSTAMP:20260511T143759
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260519T160000
DTEND;TZID=Asia/Hong_Kong:20260519T170000
DTSTAMP:20260511T143759
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
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