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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:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250513T143000
DTEND;TZID=Asia/Hong_Kong:20250513T153000
DTSTAMP:20260511T081238
CREATED:20250603T041854Z
LAST-MODIFIED:20250603T041854Z
UID:111581-1747146600-1747150200@ece.hku.hk
SUMMARY:Mixture of Experts-augmented Deep Unfolding for Activity Detection
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/95300634244 \nAbstract\nIn the realm of activity detection for massive machine-type communications\, intelligent reflecting surfaces (IRS) have shown significant potential in enhancing coverage for devices lacking direct connections to the base station (BS). However\, traditional activity detection methods are typically designed for a single type of channel model\, which does not reflect the complexities of real-world scenarios\, particularly in systems incorporating IRS. To address this challenge\, this paper introduces a novel approach that combines model-driven deep unfolding with a mixture of experts (MoE) framework. By automatically selecting one of three expert designs and applying it to the unfolded projected gradient method\, our approach eliminates the need for prior knowledge of channel types between devices and the BS. Simulation results demonstrate that the proposed MoE-augmented deep unfolding method surpasses the traditional covariance-based method and black-box neural network design\, delivering superior detection performance under mixed channel fading conditions. \nSpeaker\nMr. REN Zeyi\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nZeyi Ren received the B.Eng. degree from Beijing Institute of Technology\, Beijing\, China\, in 2023. He is currently working toward the M.Phil. degree with The University of Hong Kong\, Hong Kong. His research interests include model driven deep learning and wireless communications. \nAll are welcome!
URL:https://ece.hku.hk/events/20250513-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250512T140000
DTEND;TZID=Asia/Hong_Kong:20250512T150000
DTSTAMP:20260511T081238
CREATED:20250603T035657Z
LAST-MODIFIED:20250603T035719Z
UID:111583-1747058400-1747062000@ece.hku.hk
SUMMARY:Towards Federated and Annotation-efficient Deep Learning for Medical Image Analysis
DESCRIPTION:Zoom Link : https://hku.zoom.us/j/91765972342?pwd=CHXoKEknnfPc6zbhHCADi7A1abVUyI.1 \nAbstract\nAs deep learning is increasingly applied in medical image analysis\, developing efficient and accurate models has become crucial. However\, traditional deep learning methods usually require large amounts of annotated data\, posing a significant challenge in medical imaging due to complex data collection and high annotation costs. Furthermore\, privacy and security concerns restrict data sharing and collaboration between institutions.Federated learning (FL) and annotation-efficient techniques have emerged to address these issues. This seminar explores how combining federated learning with annotation-efficient methods can advance intelligent medical image analysis. The key topics include: 1. Annotation-efficient strategies: Discussing self-supervised and weakly-supervised learning methods to enhance training efficiency and model performance when labeled data is limited; 2. Federated learning applications: Exploring how federated learning enables distributed model training across multiple institutions without data sharing\, thereby protecting data privacy; 3. Practical applications and challenges: Analyzing specific scenarios such as disease diagnosis and organ segmentation\, discussing the strengths and limitations of federated learning and annotation-efficient techniques\, and forecasting future developments. \nSpeaker\nMr. LIN Li\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nLi LIN received his B.S. in Telecommunication Engineering from South China Normal University in 2018 and the M.S. in Information and Communication Engineering from Sun Yat-sen University in 2021. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong. \nAll are welcome!
URL:https://ece.hku.hk/events/20250512-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250509T150000
DTEND;TZID=Asia/Hong_Kong:20250509T160000
DTSTAMP:20260511T081238
CREATED:20250603T035916Z
LAST-MODIFIED:20250603T035916Z
UID:111587-1746802800-1746806400@ece.hku.hk
SUMMARY:WireLightning: Harnessing Capacitances for In-Transit Massively Parallel Matrix Multiplication
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/3837289217?omn=95617077246 \nAbstract\nAnalog computing-in-memory accelerators promise ultra-low-power\, on-device AI by reducing data transfer and energy usage. Yet inherent device variations and high energy consumption for analog-digital conversion continue to hinder their wide-scale adoption in mainstream systems. To address these issues\, this presentation will introduce WireLightning\, a novel capacitive-computing accelerator featuring a mixed-signal architecture that rethinks analog AI acceleration. Unlike conventional analog crossbars that encode weights in programmable devices\, WireLightning exploits intrinsic charge dynamics in passive capacitors\, encoding matrix multiplication through spike amplitude and timing. This design addresses critical limitations such as weight drift\, stochasticity\, and power-intensive ADC bottlenecks. Key innovations include: amplitude-temporal dual encoding that enables constant-time analog dot-products; time-based decoding scheme that significantly reduces reliance on power-intensive ADCs; row-wise parallel architecture for concurrent dot-product calculations across multiple rows to enhance throughput; and value repetition exploitation in low-bit quantized vectors to reduce multiplications to constant time complexity. A PCB prototype achieved higher accuracies than leading RRAM crossbar and PCM crossbar implementations. Implemented in a 40-nm CMOS technology\, WireLightning demonstrate superior potential in power efficiency\, while maintaining high precision. By integrating algorithm-circuit co-design with physical computing\, this work establishes capacitive computing as a promising path toward combining digital precision and analog efficiency in next-generation edge AI. \nSpeaker\nSpeaker: Mr. WANG Song\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nSong Wang received the B.Eng. degree in the Department of Automated Test and Control at Harbin Institute of Technology\, and M.Phil. degree in the Department of Mechanical Engineering at the University of Hong Kong. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, under the supervision of Prof. Hayden So. His research interests include AI chip\, computer architecture\, and reconfigurable computing. \n\nAll are welcome!
URL:https://ece.hku.hk/events/20250509-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250508T160000
DTEND;TZID=Asia/Hong_Kong:20250508T170000
DTSTAMP:20260511T081238
CREATED:20250603T024218Z
LAST-MODIFIED:20250603T025042Z
UID:111464-1746720000-1746723600@ece.hku.hk
SUMMARY:Trustworthy Image Semantic Communication with GenAI: Explainability\, Controllability\, and Efficiency
DESCRIPTION:Abstract\nImage semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission. However\, existing ISC systems based on joint source-channel coding face challenges in interpretability\, operability\, and compatibility. To address these limitations\, we propose a novel trustworthy ISC framework. This approach leverages text extraction and segmentation mapping techniques to convert images into explainable semantics\, while employing Generative Artificial Intelligence (GenAI) for multiple downstream inference tasks. We also introduce a multi-rate ISC transmission protocol that dynamically adapts to both the received explainable semantic content and specific task requirements at the receiver. Simulation results based on a real-world demo demonstrate that our framework achieves explainable learning\, decoupled training\, and compatible transmission in various application scenarios. Finally\, some intriguing research directions and application scenarios are identified. \nSpeaker\nDr. Chenyuan FENG\nMarie Skłodowska-Curie Scholar\,\n6G Star Young Scientist\,\nResearch Fellow at the University of Exeter \nSpeaker’s Biography\nDr. Chenyuan FENG\, Marie Skłodowska-Curie Scholar\, 6G Star Young Scientist. Dr. Feng earned the Ph.D. degree from the Singapore University of Technology and Design. Currently\, Dr. Feng is a Research Fellow at the University of Exeter\, U.K. Her research interests include edge intelligence and AI for communication and network. Dr. Feng has published over 40 papers\, including one ESI top 1% highly cited paper and 3 IEEE conference best papers. Moreover\, Dr. Feng has obtained five Chinese national invention patents and three edited book; earned the First Prize in International Postdoctoral Innovation and Entrepreneurship Competition\, one Gold and one Silver Awards in Chinese Internet+ Innovation and Entrepreneurship Competition; presided one EU horizon project and several National Natural Science Foundation project and national key R&D sub-project\, as well as one Enterprise Start-up Grant for Intelligent Unmanned Systems R & D Project (funded by Merchant & Investment Bureau in Chengdu Government\, China\, 5 million RMB\, as a Co-founder) and one Enterprise Start-up Grant for AI-RAN. She has served as a TPC member in numerous international conferences\, and an Associate Editor for IEEE IoTJ and IEEE OJ-COMS. \nOrganiser\nProf. Hongyang DU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250508-1/
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/2025/06/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250507T153000
DTEND;TZID=Asia/Hong_Kong:20250507T163000
DTSTAMP:20260511T081238
CREATED:20250603T025756Z
LAST-MODIFIED:20250603T041929Z
UID:111506-1746631800-1746635400@ece.hku.hk
SUMMARY:Rank-Revealing Bayesian Block-Term Tensor Completion with Graph Information
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/97776760951?pwd=zaNBWC786IgVZjQ7NU8SNDJdEeIorn.1 \nAbstract\nBlock-term decomposition (BTD)\, particularly its rank-(L_r\,L_r\,1) special case\, is widely used in signal processing. Traditional methods for computing BTD from fully observed tensors either unrealistically assume the tensor rank and block-term ranks are known or require exhaustive tuning of these parameters. While sparsity-promoting regularization has been introduced to estimate ranks more efficiently\, it still requires regularization parameter tuning. Bayesian learning addresses these issues by employing sparsity-promoting priors\, but so far is limited to fully observed BTD tensors. To process incomplete BTD tensors\, only a few optimization-based methods have been proposed\, and they continue to suffer from time-consuming tuning. To enable tuning-free BTD completion\, a novel prior is proposed here within the Bayesian framework\, and it is proved theoretically that the proposed prior induces the desired dual-level sparsity as well as graph information in the BTD model. A mean-field design is further proposed to develop a closed-form updating variational inference (VI) algorithm without loss of graph information. Extensive experiments on both synthetic datasets and real-world datasets demonstrate the superiority of the proposed method in terms of rank learning\, tensor recovery\, and factor recovery. \nSpeaker\nMr. Zhongtao Chen\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nZhongtao Chen received the B.Eng. degree from The Chinese University of Hong Kong\, Shenzhen\, China\, in 2021. He is currently working toward the Ph.D. degree with The University of Hong Kong\, Hong Kong. His research interests include signal processing and machine learning using Bayesian methods. \n\n\nAll are welcome!
URL:https://ece.hku.hk/events/20050507-3/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250507T140000
DTEND;TZID=Asia/Hong_Kong:20250507T150000
DTSTAMP:20260511T081238
CREATED:20250603T024130Z
LAST-MODIFIED:20250603T024304Z
UID:111460-1746626400-1746630000@ece.hku.hk
SUMMARY:A Novel Training Framework for Physics-informed Neural Networks: Towards Real-time Applications in Ultrafast Ultrasound Blood Flow Imaging
DESCRIPTION:Abstract\nUltrafast ultrasound blood flow imaging is a state-of-the-art technique for depiction of complex blood flow dynamics in vivo through thousands of full-view image data (or\, timestamps) acquired per second. Physics-informed Neural Network (PINN) is one of the most preeminent solvers of the Navier-Stokes equations\, widely used as the governing equation of blood flow. However\, that current approaches rely on full Navier-Stokes equations is impractical for ultrafast ultrasound. We hereby propose a novel PINN training framework for solving the Navier-Stokes equations. It involves discretizing Navier-Stokes equations into steady state and sequentially solving them with test-time adaptation. The novel training framework is coined as SeqPINN. Upon its success\, we propose a parallel training scheme for all timestamps based on averaged constant stochastic gradient descent as initialization. Uncertainty estimation through Stochastic Weight Averaging Gaussian is then used as an indicator of generalizability of the initialization. This algorithm\, named SP-PINN\, further expedites training of PINN while achieving comparable accuracy with SeqPINN. The performance of SeqPINN and SP-PINN was evaluated through finite-element simulations and in vitro phantoms of single-branch and trifurcate blood vessels. The successful implementation of SeqPINN and SP-PINN open the gate for real-time training of PINN for Navier-Stokes equations and subsequently reliable imaging-based blood flow assessment in clinical practice.\nSpeaker\nMr. Haotian Guan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong\nSpeaker’s Biography\n\nHaotian Guan received his B.S. in Applied Mathematics from The University of New Hampshire in 2019 and the M.S. in Data Science from New York University in 2021. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong.\n\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250507-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250507T100000
DTEND;TZID=Asia/Hong_Kong:20250507T110000
DTSTAMP:20260511T081238
CREATED:20250603T025022Z
LAST-MODIFIED:20250603T025022Z
UID:111484-1746612000-1746615600@ece.hku.hk
SUMMARY:Towards Ubiquitous Radio Access Using Nanodiamond Based Quantum Receivers
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/5098778281?pwd=wMZ3GQvpRdxkCjv8p79h3JN1xdgOJe.1\nMeeting ID: 509 877 8281\nPassword: 670951\nAbstract\nThe development of sixth-generation wireless communication systems demands innovative solutions to address challenges in the deployment of a large number of base stations and the detection of multi-band signals. Quantum technology\, specifically nitrogen-vacancy centers in diamonds\, offers promising potential for the development of compact\, robust receivers capable of supporting multiple users. Here we propose a multiple access scheme using fluorescent nanodiamonds containing nitrogen-vacancy centers as nano-antennas. The unique response of each nanodiamond to applied microwaves allows for distinguishable patterns of fluorescence intensities\, enabling multi-user signal demodulation. We demonstrate the effectiveness of our nanodiamonds-implemented receiver by simultaneously transmitting two uncoded digitally modulated information bit streams from two separate transmitters\, achieving a low bit error ratio. Moreover\, our design supports tunable frequency band communication and reference-free signal decoupling\, reducing communication overhead. Furthermore\, we implement a miniaturized device comprising all essential components\, highlighting its practicality as a receiver serving multiple users simultaneously. This approach enables the integration of quantum sensing technologies into future wireless communication networks.\nSpeaker\nMr. Zhang Jiahua\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong\nSpeaker’s Biography\n\nJiahua Zhang 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. Zhiqin Chu. He received his B.Eng. and M.Eng. degree in Optical Engineering from Harbin Institute of Technology (HIT)\, China\, in 2019 and 2021. His research focuses on nitrogen-vacancy (NV) centers\, diamond-based biosensing\, and thermometry.\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250507-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250506T140000
DTEND;TZID=Asia/Hong_Kong:20250506T150000
DTSTAMP:20260511T081238
CREATED:20250603T023635Z
LAST-MODIFIED:20250603T023709Z
UID:111455-1746540000-1746543600@ece.hku.hk
SUMMARY:Highly Integrated Wireless Direct Drive Motor System for Fully Enclosed Environments
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/95286760379?pwd=3C7A8QPlmXLJAQZ5IsYbEWdDXXvWk1.1\nMeeting ID: 952 8676 0379\nPassword: 492840\nAbstract\nContemporary global motor systems predominantly rely on cable-based and battery-powered energy transmission mediums\, both of which exhibit fundamental structural limitations. Wired systems face challenges such as installation and maintenance complexity\, mobility constraints\, and safety vulnerabilities\, particularly in confined spaces or high-precision applications. On the other hand\, battery-dependent systems struggle with energy density limitations\, thermal sensitivity\, and a mass penalty\, which can increase operational downtime and affect performance. These systemic deficiencies underscore the urgent need to develop next-generation motor systems that integrate contactless power transfer technologies\, wireless direct-drive control\, and passive intelligent control to overcome conventional electromechanical constraints. This study introduces a highly integrated wireless ultrasonic motor system featuring three fundamental innovations. First\, an integrated magnetic coupler is designed to realize independent decoupling control of two-phase high-frequency magnetic fields\, eliminating the dependence on cables and batteries at the receiving side. In addition\, the structural design of the receiving side is simplified to realize the synchronous transmission of wireless energy and wireless drive signals\, enabling the high-frequency electromagnetic energy (40 kHz) induced at the receiving side can directly drive the motor\, which breaks through the elimination of the rectifier and inverter link. Furthermore\, an intelligent passive control is proposed\, whereby the motor side realizes the complete elimination of components such as controllers\, sensors\, compensation capacitors\, semiconductor switches\, rectifiers\, etc.\, and the precise control of rotational speed and direction can be accomplished through the electromagnetic field modulation at the transmitter side\, resulting in a significant reduction of system complexity and cost.\nSpeaker\nMr. Zhiwei Xue\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong\nSpeaker’s Biography\nZhiwei Xue is currently working toward the Ph.D. degree in electrical and electronic engineering with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong\, China. From 2021 to 2022\, he was a Research Assistant at the Department of Electrical Engineering\, The Hong Kong Polytechnic University\, Hong Kong\, China. His research interests include wireless power transfer\, electrical machine drives\, and power electronics. \nAll are welcome!
URL:https://ece.hku.hk/events/20250506-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250506T110000
DTEND;TZID=Asia/Hong_Kong:20250506T120000
DTSTAMP:20260511T081238
CREATED:20250603T025307Z
LAST-MODIFIED:20250603T025832Z
UID:111497-1746529200-1746532800@ece.hku.hk
SUMMARY:Progressive End-to-End Object Detection in Crowded Scenes
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/92481644796?pwd=2CJcxbzAimj87HfFHoBMvUr9oCnOUZ.1\nAbstract\nCrowded object detection is a practical yet challenging research field in computer vision. Many research efforts have been made and achieved impressive progress in the last few decades. However\, most of them require handcraft components\, e.g. anchor settings and post-processing\, resulted in sub-optimal performance in handling scenes. In this work\, we propose a new query-based detection framework for crowd detection. Previous query-based detectors suffer from two drawbacks: first\, multiple predictions will be inferred for a single object\, typically in crowded scenes; second\, the performance saturates as the depth of the decoding stage increases. Benefiting from the nature of the one-to-one label assignment rule\, we propose a progressive predicting method to address the above issues. Specifically\, we first select accepted queries prone to generate true positive predictions\, then refine the rest noisy queries according to the previously accepted predictions. Experiments show that our method can significantly boost the performance of query-based detectors in crowded scenes. Moreover\, the proposed method\, robust to crowdedness\, can still obtain consistent improvements on moderately and slightly crowded datasets\, such as CityPersons and COCO. \nSpeaker\nMr. Zheng Anlin\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nMr. Anlin Zheng received the M.S. degree in computer science and technology from Beihang University\, China. He then joined Beijing Megvii Technology Co.\, Ltd. He is currently pursuing the Ph.D. degree in electrical and electronic engineering from the University of Hong Kong (HKU)\, under the supervision of Dr. Xiaojuan Qi. His research focuses on applying deep learning technology to computer vision\, including object detection and AIGC. \nAll are welcome!
URL:https://ece.hku.hk/events/20250506-3/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250506T100000
DTEND;TZID=Asia/Hong_Kong:20250506T110000
DTSTAMP:20260511T081238
CREATED:20250603T031739Z
LAST-MODIFIED:20250626T094107Z
UID:111541-1746525600-1746529200@ece.hku.hk
SUMMARY:A Diamond Heater-Thermometer Microsensor for Measuring Localized Thermal Conductivity: A Case Study in Gelatin Hydrogel
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/99573774863?pwd=rkA2rKib5AXIzhfMD5grx9darAa05B.1\nMeeting ID: 995 7377 4863\nPassword: 437351 \nAbstract\nUnderstanding the microscopic thermal effects of the hydrogel is important for its application in diverse fields\, including thermal-related studies in tissue engineering and thermal management for flexible electronic devices. In recent decades\, localized thermal properties\, such as thermal conductivity\, have often been overlooked due to technical limitations. To tackle this\, the study proposes a new hybrid diamond microsensor that is capable of simultaneous temperature control and readout in a decoupled manner. Specifically\, the sensor consists of a silicon pillar (heater) at ≈10 microns in length\, topped by a micron-sized diamond particle that contains silicon-vacancy (SiV) centers (thermometer) with 1.29 K/Hz^−0.5 temperature measurement sensitivity. Combining this innovative\, scalable sensor with a newly established simulation model that can transform heating-laser-induced temperature change into thermal conductivity\, an all-optical decoupled method is introduced with ≈0.05 W m−1 K−1 precision\, which can reduce laser crosstalk. For the first time\, the thermal conductivity change of hydrogels during the gelation process is tracked and the existence of variation is demonstrated. The study introduces a rapid\, undisturbed technique for measuring microscale thermal conductivity\, potentially serving as a valuable tool for cellular thermometry\, and highlights the idea that decoupling can reduce crosstalk from different lasers\, which is helpful for quantum sensing. \nSpeaker\nMr. Ma Linjie\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nLinjie Ma 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. Zhiqin Chu. He received his B.S. degree in Physics from Nanjing University (NJU)\, China\, in 2020. His research focuses on nitrogen-vacancy (NV) centers\, diamond-based biosensing\, and mechanobiology. \nAll are welcome!
URL:https://ece.hku.hk/events/20250506-0/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250502T100000
DTEND;TZID=Asia/Hong_Kong:20250502T110000
DTSTAMP:20260511T081238
CREATED:20250603T024005Z
LAST-MODIFIED:20250603T024005Z
UID:111459-1746180000-1746183600@ece.hku.hk
SUMMARY:Seeing Beyond Limits: Filling the Dimensionality and Visibility Gaps via the Co-design of Imaging Systems and AI Algorithms
DESCRIPTION:Mode: Online via Zoom\nMeeting ID: 944 3088 8970 | Password: 933450 \nAbstract\nConventional imaging systems face inherent dimensionality and visibility limits\, primarily because image sensors are typically two-dimensional\, and light tends to diffuse on rough surfaces or scatter within complex media. In this talk\, I will reframe imaging systems through the lens of optical encoding and neural decoding\, presenting my key contributions aimed at transcending the traditional limits of dimensionality and visibility. First\, I introduce Snapshot Compressive Imaging (SCI)\, which encodes multiple temporal\, spectral\, or angular frames into a single measurement captured by a standard two-dimensional sensor. By learning high-dimensional visual priors from image or video data\, we can efficiently reconstruct the original higher-dimensional data cube at scale. Second\, I present Privacy Dual Imaging (PDI)\, which leverages ambient light sensors embedded in most smart devices to capture images of the scene in front of the screen. This idea of seeing the invisible from subtle intensity fluctuations is inspired by George Orwell’s novel 1984\, wherein Big Brother is watching you through a two-way telescreen\, and it closely relates to incoherent lensless imaging and non-line-of-sight imaging. Lastly\, I show that large AI models\, particularly diffusion models\, can serve as generic visual priors for both cases and beyond. I aim to push the boundaries of imaging and sensing within relevant domains of AI for science and healthcare (with an example). \nSpeaker\nMr. Yang LIU\nComputer Science & Artificial Intelligence Laboratory\,\nDepartment of Electrical Engineering and Computer Science\,\nMassachusetts Institute of Technology \nSpeaker’s Biography\nYang LIU is a final-year PhD student working on AI for Imaging and AI for Science in general at MIT EECS and CSAIL with Prof. Fredo Durand. He is excited about seeing\, making\, and connecting (almost) anything. He emphasizes the co-design of AI algorithms and hardware systems for imaging and sensing beyond the native capability of visual sensors. His work has been published on top venues including Nature Materials\, Science Advances\, IEEE TPAMI\, CVPR\, and OSA Photonics Research; featured on Forbes\, WIRED\, Fox News\, and Ars Technica; contributed to a W3C working draft. He is an MIT Presidential Fellow and a Takeda Fellow. \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250502-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/06/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250430T110000
DTEND;TZID=Asia/Hong_Kong:20250430T120000
DTSTAMP:20260511T081238
CREATED:20250603T023059Z
LAST-MODIFIED:20250603T023149Z
UID:111451-1746010800-1746014400@ece.hku.hk
SUMMARY:Wide Field-of-View Imaging with Efficient Off-Axis Modeling and Encoding
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96308127770?pwd=EPYwEx3OHCPhiFDRaY9mNKFvbrtYMA.1\nMeeting ID: 963 0812 7770\nPassword: 254774 \nAbstract\nComputational optics is emerging as a transformative field to overcome challenges in achieving high-fidelity imaging across wide fields of view (FoV). However\, existing methods struggle with computational inefficiency for simulating off-axis diffraction and maintaining imaging quality at wide-FoV due to limited wavefront control. In this seminar\, I will present two synergistic advances addressing these limitations. First\, I introduce a universal angular spectrum method with optimized least-sampling criteria for off-axis diffraction modeling. Experimental results demonstrate substantial acceleration in computational speed while enabling high accuracy for ultra-wide-angle diffraction simulations. Second\, I present an end-to-end optimized framework that synergizes optical engineering and computational algorithms to transcend prior wide-FoV imaging constraints. By strategically positioning diffractive optical elements off-aperture and integrating hybrid refractive-diffractive optics with decoding multi-task networks\, we prototype two compact cameras that demonstrate high-fidelity color and depth imaging in real indoor and outdoor scenes. \nSpeaker\nMiss Haoyu Wei\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \n Speaker’s Biography\nHaoyu Wei received the M.S. degree from the Dept. of Computer Science\, Northwestern University\, Evanston\, USA\, in 2021 and B.Eng. degree from the Dept. of Computer Science\, Sichuan University\, Chengdu\, in 2019. She is currently working towards the Ph.D. degree with Dept. of Electrical and Electronic Engineering\, The University of Hong Kong (HKU)\, Hong Kong\, supervised by Prof. Edmund Y. Lam and Dr. Evan Peng. Her research interests include deep imaging systems and numerical simulations. \nAll are welcome!
URL:https://ece.hku.hk/events/20250430-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250429T110000
DTEND;TZID=Asia/Hong_Kong:20250429T120000
DTSTAMP:20260511T081238
CREATED:20250424T014238Z
LAST-MODIFIED:20250424T014321Z
UID:111218-1745924400-1745928000@ece.hku.hk
SUMMARY:EEE MasterClass (EEE 大師講堂) – III-V Compounds on Si – Combining the Best of Both Worlds
DESCRIPTION:Abstract\nGaAs/InP and related alloys\, and III-nitrides are used for most high-performance device applications except CMOS logic. Optoeletronics\, high frequency (RF to THz) and power electronics are dominated by III-V compound semiconductors. We will discuss various factors of nature\, nurture\, and culture leading to today’s landscape. Photonic integrated circuits made with compound semiconductors on native substrates are costly and limited in wafer size and throughput. There is no universal formula for combining the best of both worlds –high performance and specific functionality of compound semiconductors with the efficiency and cost-effectiveness of Si integrated circuit manufacturability. Over the years\, intense efforts have been made to incorporate high-performance III-V active devices on silicon\, to be integrated with passive components and waveguides of Si photonics. Heterogeneous integration techniques such as wafer bonding and die bonding (transfer printing) have been developed for this purpose. We have used such approaches to demonstrate and commercialize high-resolution micro-LED micro-displays. To efficiently couple light between active and passive components for Si photonics\, we recently developed a unique growth scheme – Lateral Aspect Ratio Trapping” (LART) to enable lateral selective epitaxy of device quality III-V materials right on top of the buried oxide layer of patterned silicon-on-insulator (SOI) wafers by metal organic chemical vapor deposition (MOCVD). For fully vertical GaN trench MOSs grown on Si\, balancing all the tradeoffs in terms of device structure\, performance\, process complexity and throughput is being considered. \nSpeaker\nProf. Kei May LAU\nHong Kong University of Science & Technology \nBiography of the Speaker\nKei May LAU is a Research Professor at the Hong Kong University of Science & Technology (HKUST). She received her degrees from the University of Minnesota and Rice University and served as a faculty member at the University of Massachusetts/Amherst before joining HKUST in 2000. Lau is an elected member of the US National Academy of Engineering\, a Fellow of IEEE\, Optica (formerly OSA)\, and the Hong Kong Academy of Engineering Sciences. She was also a recipient of the IPRM award\, IET J J Thomson medal for Electronics\, Optica Nick Holonyak Jr. Award\, IEEE Photonics Society Aron Kressel Award\, and Hong Kong Croucher Senior Research Fellowship. She was an Editor of the IEEE Transactions on Electron Devices and Electron Device Letters\, and an Associate Editor for the Journal of Crystal Growth and Applied Physics Letters. \nOrganiser\nProf. Han WANG\nProfessor & Associate Head (New Initiative)\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250429-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/04/1280-4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250416T140000
DTEND;TZID=Asia/Hong_Kong:20250416T170000
DTSTAMP:20260511T081238
CREATED:20250410T072506Z
LAST-MODIFIED:20250626T085202Z
UID:111109-1744812000-1744822800@ece.hku.hk
SUMMARY:Infineon Day
DESCRIPTION:Dear EEE Students\, \nYou are cordially invited to join us for the upcoming event “Infineon Day” on April 16\, 2025\, organised by Infineon and the Department of Electrical and Electronic Engineering\, The University of Hong Kong. Let you know more about the story of global semiconductor industry leader from Germany! Interesting interaction and prize-giving Q&A are waiting for you. Technical experts from Infineon will be on site to share the IOT\, Smart Campuses\, WBG technology and career development experience. \nJoin the talk and enjoy a fabulous gift! Don’t miss it! \nDate: 16 April 2025 (WED)\nTime: 2:00 pm – 5:00 pm\nVenue: Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong \nTo register\, please fill in the registration form:\nhttps://hkuems1.hku.hk/hkuems/ec_hdetail.aspx?ueid=99743 \nSchedule:\n14:00-14:30 | Registration & Infineon Demo Exhibition\n14:30-14:40 | Opening Speech by HKU\n14:40-15:00 | Infineon Company Introduction\n15:00-15:20 | Leading the Future: Infineon’s Comprehensive Solutions Empower the Consumer\, Computing\, and Communications Industries\n15:20-15:40 | Infineon’s Automotive Solutions Accelerate Smart Vehicle Evolution\n15:40-16:00 | Greening the Future: Enable decarbonization across complete energy chain\n16:00-16:15 | The Exciting Career of Electronic Engineers\n16:15-16:30 | Infineon University Program and Career Development\n16:30-16:45 | Q&A\n16:45-17:00 | Luck Draw \nBest Regards\,\nDepartment of Electrical and Electronic Engineering
URL:https://ece.hku.hk/events/20250416-0/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/04/ca.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250415T100000
DTEND;TZID=Asia/Hong_Kong:20250415T173000
DTSTAMP:20260511T081238
CREATED:20250410T072025Z
LAST-MODIFIED:20250410T072141Z
UID:111103-1744711200-1744738200@ece.hku.hk
SUMMARY:Workshop on Frontiers of Image Science and Visual Computing 2025
DESCRIPTION:You are cordially invited to join us for the upcoming workshop on “Frontiers of Image Science and Visual Computing 2025” on April 15\, 2025. For the most updated details of the workshop and registration\, please visit the event website: https://hku.welight.fun/events/workshop_25Apr \n \n \nDate: April 15\, 2025 (TUE)\nTime: 10:00 – 17:30\nVenue: Multi-purpose Zone Room\, 3/F\, Main Library\, The University of Hong Kong (HKU)\, Hong Kong SAR\nChair: Prof. Evan Yifan PENG\, HKU EEE x CS\nOrganisation: HKU WeLight Lab \nSpeakers/Guests:\n \n• David FORSYTH\, ACM Fellow\, IEEE Fellow\, University of Illinois Urbana-Champaign (UIUC)\n• Yinqiang ZHENG\, The University of Tokyo (UTokyo)\n• Seung-Hwan BEAK\, Pohang University of Science and Technology (POSTECH)\n• Yuanmu YANG\, Tsinghua University (THU)\n• He SUN\, Peking University (PKU)\n• Hongzhi WU\, Zhejiang University (ZJU)\n• Hongbo FU\, Hong Kong University of Science and Technology (HKUST)\n• Ping TAN\, Hong Kong University of Science and Technology (HKUST)\n• Tianfan XUE\, The Chinese University of Hong Kong (CUHK)\n• Wenzheng CHEN\, Peking University (PKU)\n• Xiaojuan QI\, The University of Hong Kong (HKU) \nBrown bag light lunch & tea reception will be provided. \nDetails of the workshop and registration: https://hku.welight.fun/events/workshop_25Apr\n\nLooking forward to welcoming you at the event on April 15\, 2025 (TUE).
URL:https://ece.hku.hk/events/20250415-1/
LOCATION:Multi-purpose Zone Room\, 3/F\, Main Library\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250415T093000
DTEND;TZID=Asia/Hong_Kong:20250415T103000
DTSTAMP:20260511T081238
CREATED:20250410T071656Z
LAST-MODIFIED:20250410T071700Z
UID:111099-1744709400-1744713000@ece.hku.hk
SUMMARY:Supporting Drone-based Autonomous System with Mobile-and-Edge\, Software-and-Hardware Co-Design
DESCRIPTION:Mode: Online via Zoom\nZoom Link: https://hku.zoom.us/j/93338747328\nMeeting ID: 933 3874 7328 \n \nAbstract\nDrones are among the most disruptive innovations in the past few years\, spawning many novel applications including aerial imaging\, instant delivery\, sky networking\, and industrial inspection. Building system supports for drone-based autonomous applications is critical to simultaneously enhance accuracy\, efficiency\, and minimize resource overhead. In this talk\, I will present my recent research focused on developing such system supports\, guided by a research motto “working codes on flying drones trump all hypes.” First\, I will discuss how to create “working codes suitable for flying drones”\, using GPS-denied localization as a case study to demonstrate improved system performance through algorithmic innovation. Second\, I will explore enabling “flying drones to effectively run working codes” by leveraging edge-based computational platforms\, illustrated through a collaborative drone system for industrial inspection. Third\, I will move beyond isolated algorithm design and computational platform optimizations\, discussing advancements achieved through software-hardware co-design. Finally\, I will outline future research directions aimed at advancing system performance and facilitating system deployment\, including (1) data reuse among drone control-computing-communication modules\, and (2) resource virtualization across mobile-edge-cloud infrastructures. \nSpeaker\nDr. Jingao XU\nPostdoctoral Research Associate\nCarnegie Mellon University \nBiography of the Speaker\nDr. Jingao Xu is a postdoctoral research associate at Carnegie Mellon University\, working with Prof. Mahadev Satyanarayanan. He completed his Ph.D. in Tsinghua University advised by Prof. Yunhao Liu and Prof. Zheng Yang. His research focuses on edge computing\, drone-based mobile computing and visual SLAM. He has published over 40 works in top-tier conferences and journals including NSDI\, MobiCom\, MobiSys\, Sensys\, ToN\, and TMC. He received the Honored Doctoral Dissertation Award from ACM SIGCOMM China 2022\, the Best Artifact Award at ACM MobiCom 2024. \nOrganiser\nProf. Kaibin HUANG\nProfessor & Head of Department\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250415-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250411T150000
DTEND;TZID=Asia/Hong_Kong:20250411T160000
DTSTAMP:20260511T081238
CREATED:20250410T071430Z
LAST-MODIFIED:20250410T071430Z
UID:111097-1744383600-1744387200@ece.hku.hk
SUMMARY:Wireless Permanent-Magnet Brushless DC Motor Using Contactless Feedback
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/94707594608?pwd=oSy6rRXxpXyd6bapdlmVDCtv9GRPrd.1\nMeeting ID: 947 0759 4608\nPassword: 566410 \nAbstract\nWireless power transfer (WPT) is a fast-developing technology in industrial and domestic applications. It achieves electrical and physical isolation between the power supply and load\, bringing high flexibility and safety. Based on WPT\, wireless motors allow electric motors to work in sealed environments\, however\, when using a single controller at the transmitter side\, acquiring the feedback of wireless motors is challenging because of the contactless structure. We propose a wireless permanent-magnet brushless DC motor with contactless feedback\, the rotor position measured by a wireless-powered Hall effect sensor is modulated and sensed at the transmitter side for commutation and precise speed control. Also\, the proposed system adopts hybrid modulation including PWM and sigma-delta modulated PFM (Σ-Δ PFM) to reduce the switching loss in the whole control process. Compared with existing wireless motor systems\, the proposed system realizes both power and control in the fully wireless approach and keeps good dynamic performance.  \nSpeaker\nMr. Songtao LI\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nSongtao Li received the B.Eng and M.Eng degrees in instrument science and technology from Southeast University\, Nanjing\, China in 2018 and 2021\, respectively. He is currently working toward the Ph.D. degree in electrical and electronic engineering in the University of Hong Kong\, Hong Kong\, China. His current research interests include power electronics\, wireless power transfer\, and electric vehicle technologies. \nOrganiser\nProf. Yunhe Hou\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome!\n\n——-
URL:https://ece.hku.hk/events/20250411-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250411T140000
DTEND;TZID=Asia/Hong_Kong:20250411T150000
DTSTAMP:20260511T081238
CREATED:20250410T071246Z
LAST-MODIFIED:20250410T071251Z
UID:111094-1744380000-1744383600@ece.hku.hk
SUMMARY:Mirror-Symmetrical Dijkstra’s Algorithm-Based Deep Reinforcement Learning for Dynamic Wireless Charging Navigation of Electric Vehicles
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/91849018634?pwd=YgyqXnIIfUsd8YGU2YNaSa5aj3uWou.1\nMeeting ID: 918 4901 8634\nPassword: 038419 \nAbstract\nThe dynamic wireless charging (DWC) system based on wireless charging lanes (WCLs) is an important component of smart cities\, allowing electric vehicles (EVs) to charge while moving. It is necessary to establish a user-oriented real-time DWC navigation system to achieve the joint optimization of EV routing and charging. However\, the modeling characteristics of DWC and the risk preferences of EV owners towards congested WCLs are completely different from those in traditional wired charging. Furthermore\, optimal EV charging navigation is always challenging without prior knowledge of uncertainty in electricity prices and traffic conditions. This work first proposes a novel dynamic charging routing model for individual EVs to minimize travel and charging costs\, and reformulates it as a two-step optimization problem to facilitate feature extraction. Then\, mirror-symmetrical Dijkstra’s algorithm (MSDA) is proposed to solve the reformulated model in linear time and extract advanced features from the stochastic information. By feeding the system state containing extracted features into the deep Q network (DQN) in an event-triggered manner\, the near-optimal charging navigation strategy is finally obtained. The proposed MSDA-DQN approach not only efficiently extracts low-dimensional interpretable input features\, but also adaptively learns the unknown dynamics of system uncertainty. Numerical results based on simulated and real-world data validate the proposed approach. \nSpeaker\nMiss Chaoran Si\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nChaoran Si received her bachelor degree from Tianjin University in 2018 and her master degree from Zhejiang University in 2021\, both in electrical engineering. She is currently working toward the Ph.D. degree in electrical and electronic engineering in the Department of Electrical and Electronic Engineering at the University of Hong Kong. Her current research interests include power-transportation systems\, wireless charging of electric vehicles\, and deep reinforcement learning. \nOrganiser\nProf. Yunhe Hou\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250411-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250410T163000
DTEND;TZID=Asia/Hong_Kong:20250410T173000
DTSTAMP:20260511T081238
CREATED:20250410T071105Z
LAST-MODIFIED:20250410T071105Z
UID:111090-1744302600-1744306200@ece.hku.hk
SUMMARY:Semantic-Relevance Based Sensor Selection for Edge-AI Empowered Sensing Systems
DESCRIPTION:Abstract\nThe sixth-generation (6G) mobile network is envisioned to incorporate sensing and edge artificial intelligence (AI) as two key functions. Their natural convergence leads to the emergence of Integrated Sensing and Edge AI (ISEA)\, a novel paradigm enabling real-time  acquisition and understanding of sensory information at the network edge. However\, ISEA faces a communication bottleneck due to the large number of sensors and the high dimensionality of sensory features. Traditional approaches to communication-efficient ISEA lack awareness of semantic relevance\, i.e.\, the level of relevance between sensor observations and the downstream task. In this seminar\, I will introduce a novel framework for semantic-relevance-aware sensor selection to achieve optimal end-to-end (E2E) task performance under heterogeneous sensor relevance and channel states. E2E sensing accuracy analysis is provided to characterize the sensing task performance in terms of selected sensors’ relevance scores and channel states. Building on the results\, the sensor-selection problem for accuracy maximization is formulated as an integer program and solved through a tight approximation of the objective. The optimal solution exhibits a priority-based structure\, which ranks sensors based on a priority indicator combining relevance scores and channel states and selects top-ranked sensors. Experimental results on both synthetic and real datasets show substantial accuracy gain achieved by the proposed selection scheme compared to existing benchmarks. \nSpeaker\nLIU Zhiyan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZhiyan Liu received the B.Eng. degree from the Dept. of Electronic Engineering\, Tsinghua University\, Beijing\, in 2021. He is currently working towards the Ph.D. degree with Dept. of Electrical and Electronic Engineering\, The University of Hong Kong (HKU)\, Hong Kong. His research interests include edge intelligence and distributed sensing in 6G wireless networks. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250410-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250409T110000
DTEND;TZID=Asia/Hong_Kong:20250409T120000
DTSTAMP:20260511T081238
CREATED:20250410T070435Z
LAST-MODIFIED:20250410T070813Z
UID:111088-1744196400-1744200000@ece.hku.hk
SUMMARY:Efficient Fine-Tuning and Compression of Large Language Models: Towards Low-bit and Ultra-Low Parameter Solutions
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/5995074181?omn=91841905345 \nAbstract\nEfficient fine-tuning of Large Language Models (LLMs) is crucial due to their substantial memory and computational demands. This seminar discusses recent advancements in techniques aimed at significantly reducing these costs\, enabling effective adaptation of large-scale models even on resource-constrained hardware. The talk will begin with an overview of current challenges and mainstream approaches to compressing and fine-tuning LLMs\, highlighting trade-offs between model size\, accuracy\, and efficiency. Subsequently\, the speaker will introduce novel approaches that enable fine-tuning at extremely low precision and ultra-low parameter regimes\, significantly reducing memory requirements without compromising performance. Finally\, the discussion will cover recent progress and future directions for achieving efficient deployment of LLMs in real-world applications. \nSpeaker\nMr. Jiajun Zhou\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nJiajun Zhou is currently a Ph.D. student in the Department of Electrical and Electronic Engineering at the University of Hong Kong (HKU)\, supervised by Prof. Ngai Wong\, and a visiting scholar at the University of California\, Santa Barbara (UCSB). He received his Master’s degree in IC Design Engineering from the Hong Kong University of Science and Technology (HKUST) in 2019 and a Bachelor’s degree in Integrated Circuit Design and Integrated Systems from National Huaqiao University\, China\, in 2018. He previously worked as a Research Assistant at the Chinese University of Hong Kong (CUHK). His research primarily focuses on developing innovative frameworks for efficient training and inference of Large Language Models (LLMs)\, particularly through quantization\, low-bit optimization\, and tensor decomposition. He has published extensively in AI and hardware acceleration venues\, including ACL\, NAACL\, IEEE FCCM\, and IEEE TCAD. \nOrganiser\nProf. Ngai Wong\nDepartment of Electrical and Electronic Engineering\, The University of Hong KongAll are welcome!
URL:https://ece.hku.hk/events/20250409-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250407T093000
DTEND;TZID=Asia/Hong_Kong:20250407T100000
DTSTAMP:20260511T081238
CREATED:20250410T070207Z
LAST-MODIFIED:20250410T070207Z
UID:111086-1744018200-1744020000@ece.hku.hk
SUMMARY:Opportunities from Quantum Computing for Net-Zero Power System Optimisation
DESCRIPTION:Abstract\nOptimised power system planning and operation are core to delivering a low-cost and high-reliability transition path to net-zero carbon emissions. However\, power system optimisation problems are now posing challenges for even the largest exa-scale supercomputers. A new avenue for progress has been opened by recent breakthroughs in quantum computing. Quantum computing offers a fundamentally new computational infrastructure with different capabilities and trade-offs\, and is reaching a level of maturity where\, for the first time\, a practical advantage over classical computing is available for specific applications. The talk will present emerging opportunities where quantum computing can offer value for power system optimisation\, including combinatorial\, convex and machine learning-based optimisation problems. The talk will also discuss challenges for implementation and scale-up\, and outline promising directions for future research. \nSpeaker\nProfessor Thomas MORSTYN\nAssociate Professor in Power Systems\,\nDepartment of Engineering Science\,\nUniversity of Oxford \nBiography of the Speaker\nThomas MORSTYN is an Associate Professor in Power Systems with the Department of Engineering Science\, University of Oxford and he leads the Power Systems Architecture Lab. He is a Tutorial Fellow at Hertford College\, an Honorary Fellow at the University of Edinburgh\, Associate Editor of IEEE Transactions on Power Systems and Co-Chair of the IEEE Power & Energy Society Taskforce on Power System Operations and Control with Quantum Computing. His research is focused on power system digitalisation and market design as key interlinked enablers of the net-zero transition. He received the BEng (Hon.) degree from the University of Melbourne in 2011\, and the PhD degree from the University of New South Wales in 2016\, both in electrical engineering. Previously\, He was a lecturer at the University of Edinburgh and an EPSRC research fellow at the University of Oxford. Prior to undertaking his PhD\, he also worked as an electrical engineer in Rio Tinto’s Technology and Innovation group. \nOrganiser\nProf. Yi WANG\nAssistant Professor\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAcknowledgement\nThe seminar has been supported by the Postgraduate Students Conference/Seminar Grants of the Research Grants Council\, Hong Kong. \nAll are welcome!
URL:https://ece.hku.hk/events/20250407-1/
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:20250407T090000
DTEND;TZID=Asia/Hong_Kong:20250407T153000
DTSTAMP:20260511T081238
CREATED:20250410T065951Z
LAST-MODIFIED:20250410T065951Z
UID:111079-1744016400-1744039800@ece.hku.hk
SUMMARY:Seminar on Low-Carbon and Digital Power Systems
DESCRIPTION:Click HERE to view the event poster.\n \nTime & Venue\n09:00 – 12:00: Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong (HKU);\n14:00 – 15:30: Room EH-101\, 1/F\, Eliot Hall\, HKU. \nSchedule & Speakers\n\n\n\nTime & Venue*:\nProgramme:\n\n\n09:00 – 09:20\nCB-603\nRegistration\n\n\n09:20 – 09:30\nCB-603\nOpening Remarks\n– Prof. Yi WANG\,\n  The University of Hong Kong\n\n\n09:30 – 10:00\nCB-603\nOpportunities from Quantum Computing for Net-Zero Power System Optimisation\n– Prof. Thomas MORSTYN\,\n  Oxford University\n\n\n10:00 – 10:30\nCB-603\nOnline EV Management in Smart Grids\n– Prof. Yue CHEN\,\n  The Chinese University of Hong Kong\n\n\n10:30 – 11:00\nCB-603\nUncertainty Quantification of Low-Carbon Power System Dynamics\n– Prof. Siqi BU\,\nThe Hong Kong Polytechnic University\n\n\n11:00 – 11:30\nCB-603\nLarge-scale Offshore Wind Farm Planning based on Complex Combinatorial Optimization\n– Prof. Xinwei SHEN\,\n  Tsinghua Shenzhen International Graduate School\n\n\n11:30 – 12:00\nCB-603\nData-Driven Operations for the Future Power Grid\n– Prof. Chenye WU\,\n  The Chinese University of Hong Kong\, Shenzhen\n\n\n12:00 – 14:00\nLunch and Break Time\n\n\n14:00 – 14:30\nEH-101\nSystem Strength as a Service in Large-scale Renewable Energy Projects\n– Prof. Yun LIU\,\nSouth China University of Technology\n\n\n14:30 – 15:00\nEH-101\nUrban Power System Optimization Considering Interaction with Building Clusters and Microclimates\n– Prof. Hongxun HUI\,\n  University of Macau\n\n\n15:00 – 15:30\nEH-101\nQ&A Session & Closing Remarks\n– Mr. Xueyuan CUI\,\n  The University of Hong Kong\n\n\n\n*Each 30-minute presentation should include about 25 minutes by the presenter\, and the rest is for a Q&A discussion. \nOrganisers\nProf. Yi WANG & Mr. Xueyuan CUI\nDepartment of Electrical and Electronic Engineering\, HKU \nAcknowledgement\nThe seminar has been supported by the Postgraduate Students Conference/Seminar Grants of the Research Grants Council\, Hong Kong. \nAll are welcome to join!
URL:https://ece.hku.hk/events/20250407-2/
LOCATION:Room CB-603 / EH-101
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/04/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250403T163000
DTEND;TZID=Asia/Hong_Kong:20250403T173000
DTSTAMP:20260511T081238
CREATED:20250320T064619Z
LAST-MODIFIED:20250321T014233Z
UID:110629-1743697800-1743701400@ece.hku.hk
SUMMARY:Future of MRI: Can We Learn from the Past?
DESCRIPTION:Abstract \nIn this presentation\, the speaker will cover how MRI technologies have evolved from the early pioneering days till today. The presentation will discuss the technological challenges to be met in our current pursuit of broader MRI applications in healthcare and basic biomedical research. The speaker will also discuss different application scenarios for the huge range of magnetic field strengths available today. \nSpeaker \nProfessor Juergen HENNIG\nDepartment of Radiology and Medical Physics\nUniversity Medical Center FREIBURG\, Germany \nBiography of the Speaker \nProfessor Hennig is a pioneer in MRI technology development. His research interests include MRI methodological and technological developments and their applications in clinical medicine and basic science. He has made numerous and seminal contributions to MRI technology development since the inception of MRI several decades ago. Professor Hennig received numerous international awards including Gold Medal of International Society for Magnetic Resonance in Medicine (ISMRM)\, Max Planck Award\, Houndsfield Medal for Medical Imaging\, and Einstein Professorship of the Chinese Academy of Science. He was also the past President of ISMRM. \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/20250403-1/
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:20250320T163000
DTEND;TZID=Asia/Hong_Kong:20250320T173000
DTSTAMP:20260511T081238
CREATED:20250304T090135Z
LAST-MODIFIED:20250304T090135Z
UID:110560-1742488200-1742491800@ece.hku.hk
SUMMARY:Shant Reactive Power Compensation Technologies
DESCRIPTION:Abstract\nReactive power compensation plays a critical role in improving power quality\, enhancing voltage stability\, and optimizing the efficiency of electrical power systems. This presentation will first highlight the main applications of shunt reactive power compensators and provide an overview of key technologies\, including Static Var Compensators (SVC)\, and Static Synchronous Compensators (StatComs). Then the focus will shift to StatCom\, which is considered state-of-the-art technology with superior performance. However\, the widespread adoption of high-power StatComs is hindered by cost constraints\, partly due to the large capacitor banks required in conventional Cascaded H-Bridge (CHB) multilevel converters. The presentation will discuss research advancements to highlight\, pros and cons of operating a CHB StatCom with low capacitance values. \nSpeaker\nProf. Glen FARIVAR\nNanyang Technological University (NUT Singapore) \nBiography of the Speaker\nGlen Farivar received PhD in Electrical Engineering from the University of NSW Australia in 2016. He was a Senior Research Fellow at the Energy Research Institute at the Nanyang Technological University (ERI@N) and a Co-director of the Power Electronics and Application Research Lab at Nanyang Technological University\, Singapore. Since 2023\, he has held a lecturer position\, leading power electronics research\, at the Department of Electrical and Electronic Engineering\, University of Melbourne. He is also a co-founder of SciLeap\, a platform dedicated to promoting research integrity\, accessibility\, and openness. He is a Senior Member of IEEE and co-authored over 130 papers in the areas of high-power multilevel converters and renewable energy systems. \nOrganiser\nProf. Yi WANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250320-1/
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:20250313T110000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260511T081238
CREATED:20250312T065738Z
LAST-MODIFIED:20250312T073813Z
UID:110586-1741863600-1741865400@ece.hku.hk
SUMMARY:On The Interplay of T and R in VCM-based 1T1R Structures
DESCRIPTION:Abstract\nRedox-based resistive switching random access memory (ReRAM) which is frequently discussed as a promising non-volatile memory as well as a central element in novel neuromorphic computing applications\, is typically integrated in 1-transistor-1-resistor (1T1R) structures. While the access transistor is required as a selective device and acts as an effective current compliance during SET\, it may hinder the RESET operation due to its series resistance. We showed that this may lead to a rare endurance failure. Furthermore\, the RESET speed is affected by the voltage divider of transistor and ReRAM cell\, where the initial cell resistance\, the gate voltage and the transistor geometry (i.e.\, width over length ratio w/L) are crucial. For both\, the HRS and LRS\, we demonstrate that the operation point of the 1T1R voltage divider can be shifted between the linear and the saturation regime of the transistor transfer characteristics. \nSpeaker\nDr. Stefan Wiefels\nPGI-7\, Forschungszentrum Jülich \nBiography of the Speaker\nStefan Wiefels was born in Grevenbroich\, Germany. He received the M.Sc. degree in materials science and the Ph.D. degree in electrical engineering and information technology from RWTH Aachen University\, Aachen\, Germany\, in 2016 and 2021\, respectively. His current research group is centered around the electrical characterization of memristive devices\, reaching from Redox-based resistive switches (ReRAM) to phase change memory (PCM) and from single cells (1R)\, via 1-transistor-1-resistor (1T1R) structures to arrays and circuits. A general focus lies on the automation of measurement schemes to generate significant statistics. This allows for understanding the intrinsic variability of resistive switching devices and is crucial to identify rare failure mechanisms. Further\, variability aware algorithms to program memristive devices are developed. A general target is emulating neuromorphic functionalities using external DAC and ADC before they are integrated on chip. \nOrganisers\n– Can Li\, Department of Electrical and Electronic Engineering\, The University of Hong Kong\n– Center for Advanced Semiconductor and Integrated Circuit \nAll are welcome!
URL:https://ece.hku.hk/events/20250313-3/
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:20250313T103000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260511T081238
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:20260511T081238
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:20250312T150000
DTEND;TZID=Asia/Hong_Kong:20250312T160000
DTSTAMP:20260511T081238
CREATED:20250310T012708Z
LAST-MODIFIED:20250310T012708Z
UID:110579-1741791600-1741795200@ece.hku.hk
SUMMARY:RPG Seminar – Personalized Video Fragment Recommendation
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96780307210?pwd=1wIMCFlBwNOSDiVMsEFxnVgzm5kLnn.1\nMeeting ID: 967 8030 7210\nPassword: 943198 \nAbstract\nIn the current digital landscape\, capturing user attention for long video content is increasingly challenging due to diminishing attention spans. While video production is shifting towards shorter formats\, there remains significant value in leveraging the huge volume of extant long videos to meet fast-paced user consumption habits. Addressing this challenge\, our talk presents a novel framework designed to recommend specific video fragments from long videos that align with individual user profiles. Our approach is based on three key insights: the inter-fragment contextual effect\, which leverages the complex relationships among fragments within a long video; the intra-fragment contextual effect\, which models herding effects from multi-modal signals in a single video fragment; and video-level preferences\, which ensure consistency with users’ overarching interests. Extensive experiments demonstrate that our model achieves superior performance across multiple key metrics\, outperforming state-of-the-art methods. \nSpeaker\nMr. Wang Jiaqi\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nJiaqi Wang received the B.E. degree in Computer Science and Technology from South China University of Technology. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His current research interests include recommendation systems and deep learning. \nOrganiser\nProf. Edith Ngai \nAll are welcome.
URL:https://ece.hku.hk/events/20250312-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250312T110000
DTEND;TZID=Asia/Hong_Kong:20250312T120000
DTSTAMP:20260511T081238
CREATED:20250304T094302Z
LAST-MODIFIED:20250304T094302Z
UID:110564-1741777200-1741780800@ece.hku.hk
SUMMARY:RPG Seminar – Advancing Implicit Neural Representations for Efficiency and Robustness on Hardware
DESCRIPTION:This RPg seminar will be conducted in hybrid mode via Zoom link and in venue CB-601J. \nZoom Link: https://hku.zoom.us/j/9174230511 \nAbstract\nThis seminar presents advances in hardware-efficient Implicit Neural Representations (INRs)\, addressing the significant challenges in deploying these continuous signal representations on resource-constrained platforms. Our research spans three distinct hardware environments\, each with tailored solutions. For FPGAs\, we introduce Distribution-Aware Hadamard Quantization and QuadINR with hardware-friendly quadratic activations\, demonstrating substantial efficiency gains without compromising quality. For emerging compute-in-memory (CIM) architectures\, we present novel robustness techniques—gradient-based regularization and low-rank constraints—that maintain representation fidelity despite hardware-induced perturbations. For GPU acceleration\, we showcase training innovations including progressive resolution training and frequency-aware sampling that reduce training time. \nSpeaker\nMr. Zhou Wenyong\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nWenyong Zhou received the B.E. degree in Electronic Science and Engineering from Tianjin University\, Tianjin\, China\, in 2019 and M.S. degree in Computer Engineering from Northwestern University\, Evanston\, US\, in 2021. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His current research interests include implicit neural representation and efficient model design. \nOrganiser\nProf. Ngai Wong \nAll are welcome.
URL:https://ece.hku.hk/events/20250312-1/
LOCATION:Room CB-601J\, 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:20250312T100000
DTEND;TZID=Asia/Hong_Kong:20250312T110000
DTSTAMP:20260511T081238
CREATED:20250312T012704Z
LAST-MODIFIED:20250312T012704Z
UID:110582-1741773600-1741777200@ece.hku.hk
SUMMARY:2D Materials for Next-Generation Electronics: From Low-Power Logic to Monolithic Memory
DESCRIPTION:Abstract\nSilicon has been the dominant material for electronic computing for decades and very likely will stay dominant for the foreseeable future. However\, it is well-known that Moore’s law that propelled Silicon into this dominant position is long dead. Therefore\, a fervent search for (i) new semiconductors that could directly replace silicon or (ii) new architectures with novel materials/devices added onto silicon or (iii) new physics/state-variables or a combination of above has been the subject of much of the electronic materials and devices research of the past 2 decades. The above problem is further complicated by the changing paradigm of computing from arithmetic centric to data centric in the age of billions of internet-connected devices and artificial intelligence as well as the ubiquity of computing in ever more challenging environments. Therefore\, there is a pressing need for complementing and supplementing Silicon to operate with greater efficiency\, speed and handle greater amounts of data. This is further necessary since a completely novel and paradigm changing computing platform (e.g. all optical computing or quantum computing) remains out of reach for now. The above is however not possible without fundamental innovation in new electronic materials and devices. Therefore\, in this talk\, I will try to make the case of how novel layered two-dimensional (2D) chalcogenide materials1 and three-dimensional (3D) nitride materials might present interesting avenues to overcome some of the limitations being faced by Silicon hardware. I will start by presenting our ongoing and recent work on integration of 2D chalcogenide semiconductors with silicon2 to realize low-power tunnelling field effect transistors. In particular I will focus on In-Se based 2D semiconductors2 for this application and extend discussion on them to phase-pure\, epitaxial thin-film growth over wafer scales\,3 at temperatures low-enough to be compatible with back end of line (BEOL) processing in Silicon fabs. I will then switch gears to discuss memory devices from 2D materials when integrated with emerging wurtzite structure ferroelectric nitride materials4 namely aluminium scandium nitride (AlScN). First\, I will present on Ferroelectric Field Effect Transistors (FE-FETs) made from 2D materials when integrated with AlScN and make the case for 2D semiconductors in this application.5-7 Finally\, I will end with showing our most recent results on scaling 2D/AlScN FE-FETs\, achieving ultra-high carrier and current densities8 in ferroelectrically gated MoS2 and also demonstrate negative-capacitance FETs9 by engineering the AlScN/dielectric/2D interface. \nSpeaker\nProf. Deep Jariwala\nAssociate Professor\nPeter and Susanne Armstrong Distinguished Scholar\nElectrical and Systems Engineering Primary\nMaterials Science and Engineering\nThe University of Pennsylvania \nBiography of the Speaker\nDeep Jariwala is an Associate Professor and the Peter & Susanne Armstrong Distinguished Scholar in the Electrical and Systems Engineering as well as Materials Science and Engineering at the University of Pennsylvania (Penn). Deep completed his undergraduate degree in Metallurgical Engineering from the Indian Institute of Technology in Varanasi and his Ph.D. in Materials Science and Engineering at Northwestern University. Deep was a Resnick Prize Postdoctoral Fellow at Caltech before joining Penn to start his own research group. His research interests broadly lie at the intersection of new materials\, surface science and solid-state devices for computing\, opto-electronics and energy harvesting applications in addition to the development of correlated and functional imaging techniques. Deep’s research has been widely recognized with several awards from professional societies\, funding bodies\, industries as well as private foundations\, the most notable ones being the Optica Adolph Lomb Medal\, the Bell Labs Prize\, the AVS Peter Mark Memorial Award\, IEEE Photonics Society Young Investigator Award\, IEEE Nanotechnology Council Young Investigator Award\, IUPAP Early Career Scientist Prize in Semiconductors and the Alfred P. Sloan Fellowship. He has published over 150 journal papers with more than 21000 citations and holds several patents. He serves as the Associate Editor for ACS Nano Letters and has been appointed as a Distinguished Lecturer for the IEEE Nanotechnology Council for 2025. \nOrganisers\nProfessor Han Wang\, Department of Electrical and Electronic Engineering\, HKU\nIEEE ED/SSC Hong Kong Joint Chapter \nAll are welcome!
URL:https://ece.hku.hk/events/20250312-3/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VCALENDAR