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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:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240423T140000
DTEND;TZID=Asia/Hong_Kong:20240423T150000
DTSTAMP:20260513T011630
CREATED:20240409T091859Z
LAST-MODIFIED:20250114T064707Z
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SUMMARY:Reinforcement Learning with LLMs Interaction for Edge Network-enabled Distributed Diffusion Model
DESCRIPTION:Abstract:\nIn the rapidly evolving field of generative artificial intelligence (GenAI) and AI-generated content (AIGC) services\, generative diffusion models (GDMs) have garnered widespread attention due to their extensive application and exceptional performance\, supporting a series of applications such as Stable Diffusion\, Sora\, and others. Despite their success\, the deployment of GDMs faces significant challenges\, particularly in aligning generated content with individual user preferences and ensuring production efficiency. This presentation introduces a novel user-centric interactive AI approach for edge network-enabled distributed GDM-based AIGC service framework\, prioritizing efficient and collaborative GDM deployment. Specifically\, we restructure the GDM’s inference process\, i.e.\, the denoising chain\, to enable users’ semantically similar prompts to share a portion of diffusion steps. Furthermore\, to maximize the users’ subjective quality-of-experience (QoE)\, we present a reinforcement learning with large language models interaction (RLLI) approach\, which utilizes large language model (LLM)-empowered generative agents to simulate user feedback\, providing real-time and subjective QoE feedback that reflects the spectrum of user personalities. In conclusion\, this presentation seeks to explore the reciprocal relationship between “GenAI for Network” and “Network for GenAI”\, aiming to achieve more efficient\, intelligent\, and sustainable next-generation network services. \nBiography of the Speaker:\nDr. Hongyang DU received a Ph.D. from the School of Computer Science and Engineering\, Energy Research Institute @ NTU\, Nanyang Technological University\, Singapore\, under the Interdisciplinary Graduate Program. He received the B.Sc. degree from Beijing Jiao Tong University\, Beijing\, China\, in 2021. He is the Editor-in-Chief assistant of IEEE Communications Surveys & Tutorials (2022-2024). He was recognized as an exemplary reviewer of the IEEE Transactions on Communications and IEEE Communications Letters in 2021. He was the recipient of the IEEE Daniel E. Noble Fellowship Award from the IEEE Vehicular Technology Society in 2022\, the recipient of the IEEE Signal Processing Society Scholarship from the IEEE Signal Processing Society in 2023\, the recipient of the Chinese Government Award for Outstanding Students Abroad in 2023\, and the recipient of the Singapore Data Science Consortium (SDSC) Dissertation Research Fellowship in 2023. As the team leader\, He won the Honorary Mention award in the ComSoc Student Competition from IEEE Communications Society in 2023 and the First and Second Prizes in the 2024 ComSoc Social Network Technical Committee (SNTC) Student Competition. He has published over 20 first-author papers in leading journals and flagship conferences\, such as the IEEE Journal on Selected Areas in Communications and IEEE Transactions on Mobile Computing. He has also co-authored 100+ peer-reviewed papers with a total of 1\,600+ citations. His research interests include generative artificial intelligence (GenAI)\, edge intelligence\, semantic communications\, and resource allocation. \nOrganizer: Prof. Kaibin HUANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240423-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/04/1280-3.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240423T150000
DTEND;TZID=Asia/Hong_Kong:20240423T160000
DTSTAMP:20260513T011630
CREATED:20240409T092103Z
LAST-MODIFIED:20250114T064610Z
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SUMMARY:Have AI Models Truly Simplified Healthcare? Beyond Model-centric AI\, Data- and Human-centric AI for Simplified Healthcare
DESCRIPTION:Abstract:\nIn healthcare\, the exponential growth of artificial Intelligence (AI) has led to the development of numerous advanced AI models for disease diagnosis and lesion segmentation\, etc. Despite these advancements\, the question arises: Have AI models truly simplified healthcare? In this talk\, I will share my thinking on this question\, and present my research aimed at simplifying healthcare in real-world deployment. \nTo effectively simplify healthcare\, we must go beyond just focusing on AI models. Instead\, we should consider the challenges inherent in deploying these models in real clinical practice. These challenges encompass the adaptability of AI models across diverse medical centers with varying medical imaging devices or patient populations (data-centric AI)\, alongside the interaction/collaboration between clinicians and AI models to ensure clinicians’ trust in AI model decisions (human-centric AI). \nAs for data-centric AI\, I have developed many annotation-efficient deep learning technologies to enhance model performance and efficiency when deploying in a new medical scenario\, thus reducing the burden of clinicians in large-scale dataset annotation. On the other hand\, my research on human-centric AI emphasizes the importance of close collaboration and trust between AI and clinicians\, such as enhancing communication between models and clinicians through large language models\, providing more detailed explanations for clinicians’ decision-making processes\, and deferring to radiologists when AI models fail. \nBiography of the Speaker:\nDr. Xiaoqing GUO is a postdoctoral researcher at the Department of Engineering Science\, University of Oxford. She obtained her Ph.D. degree in the Department of Electrical Engineering at the City University of Hong Kong in 2022 and received a B.S. degree from Beihang University in 2018. Her research interest is in the interdisciplinary field of AI and healthcare\, aiming to create innovative intelligent systems that can support high-quality human-machine interaction/collaboration and trustworthy clinical decision-making. In AI and medical imaging fields\, she has published over 30 top journal and conference papers\, including TPAMI\, CVPR\, ICCV\, ECCV\, TMI\, MedIA\, MICCAI\, and Nature\, reaching over 1k Google Scholar citations with an h-index of 15. She has been selected as one of the World’s Top 80 Chinese Young Female Scholars in AI\, and she has received prestigious awards\, such as CVPR Outstanding Reviewer Award\, MSRA Fellowship Nomination Award\, Outstanding Doctoral Research Award\, Outstanding Research Thesis Award\, and three year consecutive Outstanding Academic Performance Award \nOrganizer: Prof. Kaibin HUANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240423-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/04/1280-2.jpg
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