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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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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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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260306T110000
DTEND;TZID=Asia/Hong_Kong:20260306T120000
DTSTAMP:20260511T065241
CREATED:20260302T022309Z
LAST-MODIFIED:20260302T025720Z
UID:114954-1772794800-1772798400@ece.hku.hk
SUMMARY:Seminar on Cross-Species Functional MRI (fMRI) Investigations of Reinforcement Learning
DESCRIPTION:Abstract\nReinforcement learning in humans depends on distributed neural circuits for value updating and behavioural adaptation. Cross-species comparisons\, particularly with macaques\, greatly facilitate our understanding of these mechanisms in humans by revealing conserved and evolved elements\, but they crucially depend on precise anatomical alignment to identify homologous regions and interpret functional parallels or divergences across species. \nIn this talk\, I will synthesise recent cross-species fMRI evidence on prefrontal contributions to reinforcement learning. I will first outline key methods for anatomical comparison that enable functional inferences across species despite marked differences in brain morphologies. I will then present findings from reversal learning tasks in humans and macaques\, demonstrating conserved orbitofrontal cortex signals that support rapid value updating in response to changing reward contingencies. Next\, I will discuss anterior cingulate cortex (ACC) activations in both species\, which play a key role in enacting adaptive changes. Finally\, I will highlight the anatomical uniqueness of the human frontopolar cortex (FPC)\, particularly its lateral subdivision\, which lacks a clear homolog in macaques and shows emerging functional importance in our recent findings for handling higher-dimensional aspects of reinforcement learning. \nSpeaker\nProf. Bolton KH CHAU\nDepartment of Rehabilitation Sciences\,\nThe Hong Kong Polytechnic University \nSpeaker’s Biography\nProf. Bolton KH CHAU is an Associate Professor in the Department of Rehabilitation Sciences and Associate Director of the Mental Health Research Centre at The Hong Kong Polytechnic University. He received my DPhil from the University of Oxford and was APS Rising Star by the Association for Psychological Science. His research interests lie in decision neuroscience\, with a particular focus on how the brain integrates information and sometimes arrives at irrational or biased choices. He adopts a multidisciplinary approach\, combining computational modelling\, behavioural experiments\, brain imaging\, and brain stimulation to investigate the mechanisms underlying decision-making in both simple and complex contexts. Recently\, he has developed a keen interest in the frontopolar cortex\, a region uniquely expanded in the human brain\, and its role in supporting complex decision-making. This work is supported by the RGC Collaborative Research Fund. \nOrganiser\nDr. Alex Tze Lun LEONG\nDepartment of Electrical and Computer Engineering\,\nThe University of Hong Kong \nAcknowledgement\nTam Wing Fan Innovation Wing Two\n\nAll are welcome!
URL:https://ece.hku.hk/events/20260306-1/
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/2026/03/12801.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260317T100000
DTEND;TZID=Asia/Hong_Kong:20260317T110000
DTSTAMP:20260511T065241
CREATED:20260311T065221Z
LAST-MODIFIED:20260311T081220Z
UID:115308-1773741600-1773745200@ece.hku.hk
SUMMARY:Seminar on An ECE Framework for Instrumentation and Education: From Microscopy Design to Community Outreach
DESCRIPTION:Abstract\nAdvanced electron microscopy\, characterised by atomic-scale resolution\, is a cornerstone for observing material dynamics. The development of these instruments presents complex engineering challenges in electro-optics and system integration. Dr. Hsueh holding a PhD in Electrical and Computer Engineering\, leverages his expertise in electromagnetic waves\, waveguides\, and imaging theory to drive the development of next-generation electro-optical systems. This talk outlines his multidimensional approach to academia through an ECE framework. \nIn research\, Dr. Hsueh focuses on the design and development of ultrafast and quantum technologies employing scanning and transmission electron microscopy (SEM/TEM). His current work involves the commercialisation of pulsed hollow-cone hybrid electron microscopes\, a project supported by the RAISe+ scheme and protected by patents. His research experience spans laser optical design\, optical measurement systems\, optical and THz waveguide design\, optical force theory\, and aperiodic nanostructure design. Regarding teaching and administration\, Dr. Hsueh served as a Visiting Assistant Professor at the City University of Hong Kong (2023–2025)\, where he taught courses in electron microscopy\, materials science\, and engineering graphics. His ECE background further qualifies him to teach courses such as electromagnetics and other related subjects. Beyond the classroom\, he has demonstrated significant leadership in institutional service\, having organised international research conferences and contributed to the strategic planning of the university’s core facility. In the realm of knowledge transfer and outreach\, Dr. Hsueh is committed to nurturing the next generation of engineers. He is currently developing and implementing AI education programs for primary and secondary school students. By bridging high-end instrumentation design with community engagement and administrative expertise\, he aims to foster a robust and interdisciplinary academic ecosystem. \nSpeaker\nDr. Yu-Chun HSUEH\nResearch Fellow at City University of Hong Kong \nSpeaker’s Biography\nDr. Yu-Chun HSUEH received his B.S. degree in Electrical Engineering from National Tsing Hua University in 2007\, his M.S. degree from the Graduate Institute of Photonics and Optoelectronics at National Taiwan University in 2009\, and his PhD degree in Electrical and Computer Engineering from Purdue University in 2018. He was a Postdoctoral Researcher at Purdue University in 2018\, and subsequently a Postdoctoral Fellow and Research Scientist at the City University of Hong Kong from 2019 to 2023. He served as a Visiting Assistant Professor in the Departments of Materials Science and Engineering and Mechanical Engineering at the City University of Hong Kong from 2023 to 2025\, where he taught courses in electron microscopy\, materials science\, and engineering graphics. He is currently a Research Fellow at the City University of Hong Kong\, working on the commercialisation of next-generation electron microscopes and community outreach through the implementation of AI education programs for primary and secondary school students. His research experience encompasses the theory\, design\, modelling\, and measurement of photonics and optomechanics\, ranging from the terahertz (THz) to the optical regime. During his master’s program\, his research focused on low-loss THz waveguide design\, resulting in 2 journal publications and 1 patent. He was inducted as an honorary member of the Phi Tau Phi Scholastic Honor Society at National Taiwan University in 2009 and received the Government Scholarship to Study Abroad from Taiwan in 2012. During his Ph.D. program\, his research focused on the theory and modelling of field control\, field statistics\, and optomechanics with aperiodic nanostructures\, with results published in Physical Review Letters and related journals. Building on his ECE background\, his current research interests centre on the design and development of ultrafast and quantum technologies for scanning and transmission electron microscopy. He has been invited to present at international conferences and holds several patents for next-generation electron microscopes\, supported by the RAISe+ project.
URL:https://ece.hku.hk/events/20260317-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260317T143000
DTEND;TZID=Asia/Hong_Kong:20260317T153000
DTSTAMP:20260511T065241
CREATED:20260311T063203Z
LAST-MODIFIED:20260311T063203Z
UID:115304-1773757800-1773761400@ece.hku.hk
SUMMARY:Seminar on Why Not Electric Vehicle
DESCRIPTION:Abstract\nThis seminar will review some Electric Vehicle (EV) system concepts and designs\, electric machines and drives for EVs\, hybrid powertrains for hybrid EVs\, EV energy sources and energy management systems\, and EV-to-grid technology. \nSpeaker\nIr Dr. T. W. CHING\nDepartment of Electrical and Computer Engineering \nSpeaker’s Biography\nIr Dr. T. W. CHING received the Bachelor and Master degrees in Electrical Engineering from The Hong Kong Polytechnic\, and the Doctor of Philosophy in Electrical and Electronic Engineering from The University of Hong Kong. He served with the Hongkong Electric Company Limited\, CLP Power Hong Kong Limited and the University of Macau. He has been with the Department of Electrical and Computer Engineering\, The University of Hong Kong\, since 2018. He is a Chartered Electrical Engineer as well as a Chartered Building Services Engineer. In professional service\, he was a member of the Financial Committee of the IET Hong Kong and the Honorary Treasurer of Power and Energy Section of the IET Hong Kong. He was an organising committee member of the 14th\, 15th\, 16th\, 17th\, 18th and 19th Annual Power Symposium of the IET\, and the 12th APSCOM.  Internationally\, he delivered more than 100 technical presentations and served as organiser and invited chairperson of a dozen of special sessions in international conferences. His courses are “Electric Vehicle Technology”\, “Electrical Installations” and “Advanced Electric Vehicle Technology”. Recently\, he created two master courses\, namely “Advanced electrical energy & power conversion systems” and “Advanced optimisation & control strategies in modern power systems”.  He also co-supervises PhD students in his areas of expertise.
URL:https://ece.hku.hk/events/20260317-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260319T151500
DTEND;TZID=Asia/Hong_Kong:20260319T161500
DTSTAMP:20260511T065241
CREATED:20260309T094107Z
LAST-MODIFIED:20260309T094107Z
UID:115278-1773933300-1773936900@ece.hku.hk
SUMMARY:Seminar on Integration of Renewable Energy for Power Restoration: Real-time Digital Simulation Approach
DESCRIPTION:Abstract\nThe drive toward aggressive decarbonization goals is rapidly transforming the power grid\, highlighted by an increase in renewable energy production. This expansion relies heavily on Distributed Energy Resources (DERs)\, yet operators face challenges due to the lack of transparency in DER operations. This opacity poses significant risks to grid stability as the growing number of DERs could exceed the capacity of the current power network. In response\, the emergence of Digital Twins (DT) technology provides a potential solution by creating virtual replicas of the physical grid infrastructure\, which require minimal data transmission. DT technology overcomes the obstacles of real-time data flow and enhances system transparency. To encourage the wider application of DT in the industry\, it is crucial to develop and test its applications through practical experiments. For this purpose\, Power Hardware-in-the-Loop (PHIL) experiments are used to compare the effectiveness of real power components with DT models. These experiments connect Grid-forming Inverter (GFMI) to a Real-time Digital Simulator (RTDS) for PHIL and DT testing\, enabling detailed analysis of photovoltaic inverter behaviour. \nThis research presents a platform specifically built for immediate simulation suited to DT and PHIL methods. It is designed to prototype\, demonstrate\, and assess GFMIs under various critical scenarios for power restoration. By incorporating the Perez Model into the DT model through simulation exchange\, the accuracy in comparison with the traditional PHIL model is enhanced. Thus\, the entire restoration process can be thoroughly represented and analysed. All in all\, this paper introduces a novel approach to integrating renewable energy resources using PHIL-based digital twins technology to enhance power restoration stability. \nSpeaker\nDr. Jason Man Hin CHOW\nLecturer at Vocational Training Council (VTC) \nSpeaker’s Biography\nDr. Jason Man Hin CHOW obtained a BEng from the University of Sheffield and an MSc and a PhD from The University of Hong Kong\, all in Electrical and Electronic Engineering. He is now a Lecturer at Vocational Training Council (VTC) and has over 4 years of teaching experience in territory education. Before joining VTC\, he joined an international consultancy firm to undergo a 2-year formal training programme for professional development. He was subsequently promoted to Project Engineer in charge of several large-scale electrical installation projects. Appointed as Deputy Manager of CLP Power Engineering Laboratory under VTC jurisdiction\, he leads a team of lecturers and laboratory technicians to do experiments/projects and research in collaboration with other universities. He is a Chartered Engineer\, Beam Pro\, Member of IET\, Member of InstMC\, Member of HKIE\, Member of CIBSE and Member of Building Services Operation\, Maintenance and Executives Society. Dr. Chow is actively participating in local professional institutions\, and he has published several conference/journal papers at international organisations/institutions.  His research areas include power system control\, integration of renewable energy and smart grid.
URL:https://ece.hku.hk/events/20260319-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260320T090000
DTEND;TZID=Asia/Hong_Kong:20260320T100000
DTSTAMP:20260511T065241
CREATED:20260311T024703Z
LAST-MODIFIED:20260311T024703Z
UID:115299-1773997200-1774000800@ece.hku.hk
SUMMARY:RPG Seminar – Synthetic Aperture for High Spatial Resolution Acoustoelectric Imaging
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/97208194193 \nAbstract\nAcoustoelectric imaging (AEI) refers to the mapping of electric fields in electrolyte and tissue media by measuring the acoustoelectric (AE) effect. It shows promise for non-invasive electrophysiological mapping down to the resolution of diagnostic ultrasound imaging. AE signals are typically induced by applying focused ultrasound (FUS) waves\, which sift out the electric signals at a defined focal spot. However\, the spatial resolution of FUS-AEI is limited by the finite focal extent. To achieve improved AEI spatial resolution across the full imaging depth\, we propose to perform AEI by adopting a Synthetic Aperture (SA) approach. SA-AEI images were reconstructed through pixel-oriented delay-and-sum of the unfocused AE signals. Experiments were done on an NaCl volume and an ex vivo lobster nerve. Overall\, SA-AEI exhibited superior lateral resolution compared to FUS-AEI\, particularly for electric targets outside the focal zone of FUS-AEI. Due to inherently lower SNR of the SA approach\, we further proposed coherence-based beamforming to enhance the image quality of SA-AEI images. We envision that proposed SA-AEI would be a useful strategy for AEI\, when spatial resolution is the top imaging performance criterion and prior locations of bioelectric sources are unknown. \nSpeaker\nMr. Wei Yi Oon\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nWei Yi OON received his BEng in Medical Engineering from The University of Hong Kong in 2021. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Hong Kong\, with a research focus on acoustoelectric imaging. \nOrganiser\nProf. Wei-Ning Lee\nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260320/
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260324T140000
DTEND;TZID=Asia/Hong_Kong:20260324T150000
DTSTAMP:20260511T065241
CREATED:20260320T093741Z
LAST-MODIFIED:20260320T093741Z
UID:115337-1774360800-1774364400@ece.hku.hk
SUMMARY:RPG Seminar – LLMs for Social Good: Addressing Data Scarcity and Opacity for Alzheimer’s Diagnosis and Prognosis
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/94842355191?pwd=02bHCUfep3119O1jbeDHbnZNKaKUJ8.1 \nAbstract\nEarly detection of Alzheimer’s Disease (AD) through non-invasive speech analysis offers a highly promising diagnostic avenue. However\, the development of robust computational models is severely hindered by the fundamental imperfections of real-world clinical data. Spontaneous patient speech is often noisy and highly variable\, while longitudinal clinical records suffer from severe data scarcity\, temporal sparsity\, and missing values. Consequently\, traditional deep learning models act as opaque “black boxes\,” and this inherent opacity undermines the clinical trust required for real-world deployment. Furthermore\, while Large Language Models (LLMs) show revolutionary potential\, they too struggle to robustly model individualized disease progression from sparse data without specialized architectural integration. This leads to the central research question: How can an LLM-driven framework be systematically designed to extract clinically meaningful features and synthesize high-fidelity multi-modal data\, thereby overcoming the intertwined limitations of data incompleteness and black-box opacity? \nTo address this\, this seminar proposes an LLM-driven spatio-temporal multi-modal framework. The overarching objective is to develop theoretically grounded methodologies that leverage LLMs to robustly distill raw patient speech into structured Cognitive-Linguistic (CL) atoms and interpretable linguistic markers. Concurrently\, the framework integrates qualitative medical knowledge and synthesizes rich\, realistic training samples to effectively enrich decision boundaries in data-deficient environments. This research significantly advances AI for Social Good by providing a scalable\, low-cost methodology for early dementia screening that reduces the reliance on invasive and expensive traditional diagnostics. \nSpeaker\nMr. Tingyu MO\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMr. Tingyu MO is a Ph.D. candidate with the Advanced Well-being and Society Research Platform (AI-WiSe) at The University of Hong Kong\, under the supervision of Prof. Victor O.K. Li\, Prof. Jacqueline C.K. Lam\, and Prof. Yunhe Hou. He received his B.S. degree in Intelligence Science and Technology from the University of Science and Technology Beijing in 2021\, and his M.Eng. degree in Electronic and Information Engineering from Beihang University. His research interests include AI for Social Good\, with a specific focus on Alzheimer’s diagnosis and prognosis. \nOrganiser\nProf. Victor O.K Li\, Prof. Jacqueline C.K Lam\, Prof. Yunhe Hou\nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260324/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20260326T170000
DTEND;TZID=Asia/Hong_Kong:20260326T180000
DTSTAMP:20260511T065241
CREATED:20260320T094513Z
LAST-MODIFIED:20260320T094513Z
UID:115340-1774544400-1774548000@ece.hku.hk
SUMMARY:RPG Seminar – Scaling Up Spatial Awareness: High-Fidelity Data Synthesis for 3D Scene Understanding
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/91627715757?pwd=ByKZvbK3QYx8VSWXVoNGBsZXTpFEz3.1 \nAbstract\nSpatial understanding constitutes a fundamental pillar of human-level intelligence\, yet its advancement is currently bottlenecked by the scarcity of diverse\, high-fidelity 3D data. Existing research predominantly relies on domain-specific or manually annotated datasets\, creating a critical void: the absence of a principled\, scalable engine capable of synthesizing high-quality spatial data at scale. To address this\, we elucidate the core design principles for robust spatial data generation and introduce OpenSpatial—an open-source engine engineered for high fidelity\, massive scalability\, and broad task diversity. OpenSpatial adopts 3D bounding boxes as the foundational primitive to architect a comprehensive data hierarchy across five essential dimensions: Spatial Measurement\, Spatial Relationship\, Camera Perception\, Multi-view Consistency\, and Scene-Aware Reasoning. Leveraging this infrastructure\, we curate OpenSpatial-3M\, a large-scale dataset that enables models to transition from simple recognition to sophisticated spatial intelligence. Extensive evaluations demonstrate that models trained on our synthesized data achieve state-of-the-art performance across a wide spectrum of benchmarks\, showing substantial and consistent improvements over existing baselines. Furthermore\, we provide a systematic analysis of how synthesized data attributes influence the emergence of spatial perception in vision-language models. By open-sourcing both the engine and the 3M-scale dataset\, we offer a versatile foundation to accelerate future research in generalized 3D scene understanding. \nSpeaker\nMr. Jianhui Liu\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMr. Jianhui Liu is a PhD candidate with the Department of Electrical and Electronic Engineering at the University of Hong Kong. He received the B.Eng. degree in Intelligent Science and Technology from Xidian University in 2021. His research interest lies in machine learning and computer vision\, focusing on Multimodal Large Language Models (MLLMs) for reasoning\, agent\, long video\, spatial intelligence\, unified models\, and their real-world grounding and applications. \nOrganiser\nProf. Xiaojuan Qi\nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260326/
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:20260331T140000
DTEND;TZID=Asia/Hong_Kong:20260331T150000
DTSTAMP:20260511T065241
CREATED:20260327T023015Z
LAST-MODIFIED:20260327T023015Z
UID:115430-1774965600-1774969200@ece.hku.hk
SUMMARY:RPG Seminar – Broadband Mamyshev Oscillator at 1.7 μm for Multicolor Three-photon Fluorescence Microscopy
DESCRIPTION:Zoom Link:\nhttps://hku.zoom.us/j/95690107722?pwd=KARbsSPDxqYtCaSbRQ0AbxNFmlyBl2.1 \nAbstract\nThree-photon fluorescence (3PF) microscopy enables high-contrast deep-tissue imaging with cellular resolution\, especially in 1.7 μm wavelength range\, yet its widespread adoption has been hindered by the lack of compact\, tunable\, and high-power femtosecond laser sources. Here\, we demonstrate a broadband tunable ultrafast Mamyshev oscillator operating in the 1.7 μm wavelength region\, specifically designed for multicolor 3PF microscopy. The all-fiber ring cavity\, incorporating two arms with tunable grating-based filters\, generates stable ultrashort pulses with flexibly tunable central wavelength from 1730 nm to 1810 nm and adjustable bandwidth up to 140 nm at 10 dB. The oscillator at 7.14-MHz repetition rate are amplified using a chirped pulse amplification (CPA) system to achieve 80-nJ pulses with a slope efficiency of 46.2%\, and finally compressed to 65 fs. We showcase the versatility of this laser source through various imaging modalities. High-contrast\, label-free third-harmonic generation (THG) images of diverse biological samples are presented. Deep-tissue vasculature 3PF images in an ex vivo mouse brain down to a depth of 1 mm are visualized. Crucially\, we achieve multicolor 3PF imaging with a single excitation wavelength for various co-labeled mouse brain samples\, visualizing the interaction between neurons and plaques with distinct morphologies in an Alzheimer’s disease mouse model. This compact\, tunable\, and high-power 1.7 μm ultrafast fiber laser establishes a powerful tool for advanced biomedical imaging\, particularly for deep tissue and multiplexed studies of neurodegenerative diseases. \nSpeaker\nMiss Xiaoxiao Wen\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nXiaoxiao Wen received her bachelor’s degree and the master’s degree from the South China University of Technology (SCUT) in 2019 and 2022\, specializing in ultrafast laser dynamics measurement. She is currently a PhD candidate at the Department of Electrical and Electronic Engineering\, the University of Hong Kong\, under the supervision of Prof. Kenneth Kin-Yip Wong. Her current research interests include ultrafast fiber laser\, fiber nonlinearities\, ultrafast measurement\, multiphoton microscopy\, and optical neural networks. \nOrganiser\nProf. Kenneth Kin-Yip Wong \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260331/
CATEGORIES:Seminar
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