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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART;TZID=Asia/Hong_Kong:20250709T160000
DTEND;TZID=Asia/Hong_Kong:20250709T170000
DTSTAMP:20260509T153812
CREATED:20250707T114033Z
LAST-MODIFIED:20250708T020744Z
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SUMMARY:Seminar on AI for 6G Communications
DESCRIPTION:Abstract\nIntelligent reflecting surface (IRS) is envisioned to be a promising 6G technology which changes wireless communications from “adapting to wireless channels” to “changing wireless channels”. However\, current IRS configuration schemes\, consisting of sub-channel estimation and passive beamforming\, are model-based designs and are difficult to be realized in practical and complex radio environment. To create the smart radio environment\, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Radio map technology offers a refined solution to reduce MIMO beamforming’s dependency on channel state information (CSI). We introduce a deep learning-based approach to generate radio maps directly from raw CSI data of MIMO systems\, presenting two baseline schemes—one predictive and another based on throughput. An end-to-end architecture\, tailored to MIMO beamforming vectors from location data\, is proposed to employ deep neural networks through a task-oriented design and a customized loss function. Our numerical results highlight the advantages of this approach\, suggesting the potential to replace MIMO CSI with location data. \nSpeaker\nProf. Wei ZHANG\nProfessor\,\nSchool of Electrical Engineering and Telecommunications\,\nThe University of New South Wales\nVice President\, IEEE Communications Society \nSpeaker’s Biography\nWei Zhang (F’15) is Vice President of IEEE Communications Society. He received the Ph.D. degree from the Chinese University of Hong Kong in 2005. Currently\, he is a professor at the School of Electrical Engineering and Telecommunications\, the University of New South Wales\, Sydney\, Australia. His current research interests include 6G communications. He has been an IEEE Fellow since 2015 and was an IEEE ComSoc Distinguished Lecturer in 2016-2017. Within the IEEE ComSoc\, he has taken many leadership positions including Chair of Wireless Communications Technical Committee (2019-2020)\, Vice Director of Asia Pacific Board (2016-2021)\, Editor-in-Chief of IEEE Wireless Communications Letters (2016-2019)\, Member-at-Large on the Board of Governors (2018-2020)\, Technical Program Committee Chair of APCC 2017 and ICCC 2019 and 2024\, Award Committee Chair of Asia Pacific Board and Award Committee Chair of Technical Committee on Cognitive Networks. \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250709-1/
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/2025/07/1280.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250715T160000
DTEND;TZID=Asia/Hong_Kong:20250715T170000
DTSTAMP:20260509T153812
CREATED:20250709T033608Z
LAST-MODIFIED:20250709T033659Z
UID:112605-1752595200-1752598800@ece.hku.hk
SUMMARY:Seminar on 6G Wireless Enabled Autonomous Driving and Transportation of Future
DESCRIPTION:Abstract\nThe forthcoming sixth generation (6G) wireless networks will be one of the galvanizing technologies for future advanced and autonomous transportation systems. 6G wireless will provide communication services with stringent requirements which are necessary for autonomous driving and on-flight Internet connectivity. Introduction of AI-empowered 6G would guarantee a more intelligent\, efficient and secure transportation system. In this talk\, some of the new usage scenarios and capabilities of the 6G compared with the existing cellular networks will be outlined\, followed by description of its potential and challenges for seamless and ubiquitous connectivity across the heterogeneous and multi-layer transportation systems. \nSpeaker\nProf. Abbas JAMALIPOUR\nPhD\, Fellow IEEE\, Fellow IEICE\, Fellow IEA\, Fellow AIIA\nChair Professor of Ubiquitous Mobile Networking\, The University of Sydney\nEditor-in-Chief\, IEEE Transactions on Vehicular Technology\nPast President\, IEEE Vehicular Technology Society \nSpeaker’s Biography\nProf. Abbas JAMALIPOUR is the Chair Professor of Ubiquitous Mobile Networking at The University of Sydney and the Editor-in-Chief\, IEEE Transactions on Vehicular Technology. He holds a PhD in Electrical Engineering from Nagoya University\, Japan; and is a Fellow of the IEEE\, IEICE\, Engineers Australia\, AIIA\, and a Visiting Fellow of the Royal Academy of Engineering. He has authored nine technical books\, eleven book chapters\, over 650 technical papers\, and five patents\, all in the field of wireless communications. He was the President (2020-21)\, Executive Vice-President (2018-19)\, and has been an elected voting member of the Board of Governors of the IEEE Vehicular Technology Society since 2014. Previously\, he served as the Editor-in-Chief IEEE Wireless Communications\, Vice President-Conferences\, and a member of Board of Governors of the IEEE Communications Society. He is on the editorial board of the IEEE Access\, member of the Advisory Board of IEEE Internet of Things Journal\, and an editor for several other journals. He is the recipient of several prestigious awards such as the IEEE ComSoc Harold Sobol Award\, the IEEE ComSoc Best Tutorial Paper Award\, as well as over fifteen Best Paper Awards. \nOrganiser\nProf. Hongyang DU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong
URL:https://ece.hku.hk/events/20250715-1/
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/2025/07/1280-1.jpg
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20250719
DTEND;VALUE=DATE:20250720
DTSTAMP:20260509T153812
CREATED:20250326T030136Z
LAST-MODIFIED:20250625T063840Z
UID:110671-1752883200-1752969599@ece.hku.hk
SUMMARY:HKU-EEE RoboLeague 2025 港大電機電子工程：中學機械人競技聯盟2025
DESCRIPTION:🚀 Join the Excitement of Robotics! \nCompetition Details\nDate: 19 July 2025 (SAT)\nTime: Check-in begins at 9:00 AM\nVenue: Innovation Wing One\, G/F\, Hui Oi Chow Science Building\, The University of Hong Kong (HKU) (directions to the venue) \nCompetition Description\nEngage your autonomous robots in thrilling challenges:\n– Maze Navigation 迷宮探索 ​\n– Rescue Missions 搜救任務 ​\n– Soccer League – Standard Platform 足球標準平台組 ​\n– Soccer League – Open Platform 足球公開組 ​ \nWho Can Join?\nTarget Audience: Secondary School Students Team Formation: No more than 4 students ( any Forms ) per team from the same school \nKey Dates\n*Briefing Session: \n– Date: 26 April 2025 (SAT) @11:00AM \n– Venue: Room 603\, 6/F\, Chow Yei Ching Building\, HKU \n*Application Deadline: \n– Date: 30 April 2025 (WED) @11:59PM \nRegistration & Details\nhttps://linktr.ee/hkeeerl
URL:https://ece.hku.hk/events/20250426-1/
LOCATION:Tam Wing Fan Innovation Wing One\, G/F\, Hui Oi Chow Science Building\, The University of Hong Kong (HKU)
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/03/banner.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250724T143000
DTEND;TZID=Asia/Hong_Kong:20250724T153000
DTSTAMP:20260509T153812
CREATED:20250722T064431Z
LAST-MODIFIED:20250722T064800Z
UID:112757-1753367400-1753371000@ece.hku.hk
SUMMARY:Seminar on Symmetric Diffusers: Learning Discrete Diffusion on Finite Symmetric Groups
DESCRIPTION:Abstract\nFinite symmetric groups Sn are essential in fields such as combinatorics\, physics\, and chemistry. However\, learning a probability distribution over Sn poses significant challenges due to its intractable size and discrete nature. We introduce SymmetricDiffusers\, a novel discrete diffusion model that simplifies the task of learning a complicated distribution over Sn by decomposing it into learning simpler transitions of the reverse diffusion using deep neural networks. We identify the riffle shuffle as an effective forward transition and provide empirical guidelines for selecting the diffusion length based on the theory of random walks on finite groups. Additionally\, we propose a generalized Plackett-Luce (PL) distribution for the reverse transition\, which is provably more expressive than the PL distribution. We further introduce a theoretically grounded “denoising schedule” to improve sampling and learning efficiency. Extensive experiments show that our model achieves state-of-the-art or comparable performances on solving tasks including sorting 4-digit MNIST images\, jigsaw puzzles\, and traveling salesman problems. \nSpeaker\nProf. Renjie LIAO\nDepartment of Electrical and Computer Engineering\, and\nDepartment of Computer Science\,\nUniversity of British Columbia (UBC) \nSpeaker’s Biography\nRenjie Liao is an Assistant Professor in the Department of Electrical and Computer Engineering and an Associate Member of the Department of Computer Science at the University of British Columbia (UBC). He is also a faculty member at the Vector Institute and holds a Canada CIFAR AI Chair. Prior to joining UBC\, he was a Visiting Faculty Researcher at Google Brain\, working with Geoffrey Hinton and David Fleet. He received his Ph.D. in Computer Science from the University of Toronto in 2021\, under the supervision of Richard Zemel and Raquel Urtasun. During his Ph.D.\, he also worked as a Senior Research Scientist at Uber Advanced Technologies Group. He holds an M.Phil. in Computer Science from the Chinese University of Hong Kong (2015) and a B.Eng. in Automation from Beihang University (2011). His research interests span machine learning and its intersection with computer vision\, self-driving\, healthcare\, and beyond\, with a particular focus on probabilistic and geometric deep learning. \nOrganiser\nProf. Xiaojuan QI\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250724-1/
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/2025/07/1280-2.jpg
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