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X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250808T140000
DTEND;TZID=Asia/Hong_Kong:20250808T160000
DTSTAMP:20260509T231326
CREATED:20250801T021445Z
LAST-MODIFIED:20250804T020549Z
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SUMMARY:Seminar on Publishing in Nature Nanotechnology: An insider’s view of Nature journals
DESCRIPTION:Abstract\nDr. Lu will talk about the scope of Nature Nanotechnology and an overview of Nature journals. And she will share her experience on the editorial process (the workflow and the statistical data) and disclose the criteria of Nature journals from an insider’s view. Through the talk\, you will know better on what kind of papers a highly selective journal is looking for\, how the editors decide whether to send a paper out or not\, what they will do when the reviewers’ comments are contradictory. And Lu is happy to share with you her personal experience as an editor if you are interested together with some practical tips for writing and submitting a paper to a Nature journal. \nSpeaker\nDr. Lu Shi\nSenior Editor\nSpringer Nature \nSpeaker’s Biography\nDr. Lu Shi joined Nature Nanotechnology in April 2023 from Wiley\, where she was the Editor-in-Chief of Advanced Electronic Materials and Deputy Editor of Advanced Materials. Prior to her editorial career\, she worked on 2D materials growth and magnetoelectric transport behavior of van der Waals heterostructures. For her work\, she received a joint PhD in Materials Science and Physics from the Université Catholique de Louvain\, Belgium and Université Grenoble Alpes\, France. She obtained her BEng and MSc in Materials Science and Engineering from Shanghai Jiao Tong University and Shanghai Institute of Ceramics\, Chinese Academy of Sciences. She covers a broad range of topics across electronics and optoelectronics in the journal and is based in Shanghai. \nOrganiser\nProf. Can Li\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250808-1/
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/2025/08/Can-Li_20250808-seminar-web-banner.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250814T160000
DTEND;TZID=Asia/Hong_Kong:20250814T173000
DTSTAMP:20260509T231326
CREATED:20250813T012141Z
LAST-MODIFIED:20250813T013434Z
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SUMMARY:Seminar on Voltage Stability Constrained Power System Optimization: A Constraint-learning Method
DESCRIPTION:Abstract\nHigh penetration of renewable energy poses severe challenges to power system voltage stability due to weakened voltage and reactive power support\, complex voltage stability mechanisms\, and highly diversified operation states. Traditional control strategies predefined based on limited operation states fail to maintain the required voltage stability margin. This report presents a constraint-learning-based framework for embedding accurate and efficient voltage stability constraints into power system optimization to improve voltage stability of the optimization results. The proposed framework represents the inherently nonlinear and nonconvex voltage stability constraints using multiple convex polyhedrons (MCPH). The learned constraints are linear\, sparse\, and embeddable in mixed-integer linear programming (MILP) formulations. Case studies demonstrate that integrating MCPH constraints into voltage stability constrained unit commitment improves voltage stability margin with reduced computational burden. Furthermore\, the framework is extended to generation expansion planning\, where physical ensemble constraint learning captures converter-driven stability requirements across diverse generation mix scenarios. The results confirm that the proposed approach effectively balances stability enhancement\, solution efficiency\, and scalability for high-renewable\, stability-constrained power system optimization. \nSpeaker\nMr. Hongyang Jia\nTsinghua University \nSpeaker’s Biography\nHongyang Jia received the B.S. degree in electrical engineering in 2021 from Tsinghua University\, Beijing\, China\, where he is currently pursuing the Ph.D. degree in Electrical Engineering under the supervision of Associate Prof. Ning Zhang. He was a Visiting Ph.D. Student at Imperial College London (Aug. 2024 – Feb. 2025) under Associate Prof. Fei Teng. His research interests center on trustworthy machine learning for science and engineering\, with specific applications in power system optimization under voltage stability constraints\, data-driven security/stability rule extraction and embedding\, and power system voltage stability assessment. \nOrganiser\nProf. Wang Yi \nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250814-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=application/pdf:https://ece.hku.hk/wp-content/uploads/2025/08/August-14-2025-1-Web-banner.pdf
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250815T110000
DTEND;TZID=Asia/Hong_Kong:20250815T120000
DTSTAMP:20260509T231326
CREATED:20250806T043442Z
LAST-MODIFIED:20250806T090812Z
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SUMMARY:Seminar on Towards Deep Learning MR Reconstruction with No Ground Truth and Fast Inference
DESCRIPTION:Abstract\nSince 2016\, deep learning techniques have been introduced to solve the inverse problem of MR image reconstruction from undersampled data from accelerated acquisitions. Since then\, the field has grown substantially. A wide range of machine learning methods have been developed\, translated into clinical practice and adopted as products by all major scanner vendors. In this talk\, after a general introduction to deep learning for MR image reconstruction\, I will focus on two open challenges in the field. First\, the application of deep learning reconstruction for dynamic contrast-enhanced imaging and abdominal imaging\, where no ground truth can be obtained for model training. Second\, the optimisation of network architectures towards computation time at inference for real-time imaging and clinical translation of instance-specific learning\, where trainings need to be performed during inference. \nSpeaker\nProf. Florian KNOLL\nProfessor and Head of the Computational Imaging Lab\,\nDepartment Artificial Intelligence in Biomedical Engineering (AIBE)\,\nFriedrich-Alexander-Universität Erlangen-Nürnberg \nSpeaker’s Biography\nProf. Florian KNOLL received his PhD in Electrical Engineering in 2011 from Graz University of Technology. From 2015 to 2021\, he was Assistant Professor for Radiology at the Center for Biomedical Imaging at NYU Grossman School of Medicine. Since 2021\, he has been Professor and Head of the Computational Imaging Lab at the Department Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg. He currently holds four grants from the German Research Fund (DFG) and an R01 grant from the National Institutes of Health (NIH). His research interests include iterative MR image reconstruction\, parallel MR imaging\, compressed sensing and machine learning. \nOrganiser\nProf. Ed Xuekui WU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250815-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/08/1280.jpg
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