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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:20250101T000000
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DTSTART;TZID=Asia/Hong_Kong:20260515T160000
DTEND;TZID=Asia/Hong_Kong:20260515T170000
DTSTAMP:20260513T121900
CREATED:20260511T020048Z
LAST-MODIFIED:20260511T020048Z
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SUMMARY:RPG Seminar – Unveiling the Relationship Between Cation Content and Zeta Potential of Colloids for Forming High-Quality Perovskites
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/94017530661 \nAbstract\nThere are numerous studies focusing on the crystallization dynamics of perovskite materials. However\, the change of precursor properties which can also significantly affect crystallization behavior\, is always ignored. In this seminar\, we establish a comprehensive understanding of the relationship between A-site cations content and zeta potential of precursor\, revealing its influence on perovskite formation and crystallization dynamics. Through in-situ photoluminescence (PL) and X-ray diffraction (XRD) analyses\, we demonstrate how zeta potential impacts the formation process and crystallization behavior of perovskites. Furthermore\, we explore the effects of zeta potential on the optical and electrical properties of the resulting materials. Our findings indicate that achieving a zeta potential near zero facilitates the fabrication of high-quality and additive-free perovskites\, leading to enhanced performance in perovskite solar cells (PSCs) and perovskite light-emitting diodes (PeLEDs). This work provides vital insights into tuning interfacial properties for improved perovskite optoelectronic devices. \nSpeaker\nMr. Qi XIONG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nQi Xiong received the B.S. degree in Polymer Materials and Engineering from Hainan University\, and the M.S. degree in Material Science and Engineering from South China University of Technology. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering\, Faculty of Engineering\, The University of Hong Kong. His current research interests include perovskite synthesis and blue perovskite light-emitting diodes (PeLEDs). \nOrganiser\nProf. Wallace C.H. CHOY \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260515/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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DTSTART;TZID=Asia/Hong_Kong:20260518T160000
DTEND;TZID=Asia/Hong_Kong:20260518T170000
DTSTAMP:20260513T121900
CREATED:20260513T040826Z
LAST-MODIFIED:20260513T041100Z
UID:115975-1779120000-1779123600@ece.hku.hk
SUMMARY:RPG Seminar – Robust Multivariate Autoregressive Model Estimation under Impulsive Noise
DESCRIPTION:Zoom Link \nhttps://hku.zoom.us/j/9995553636?omn=96185323037 \nAbstract\nThis seminar presents robust multivariate autoregressive (MVAR) model estimation under impulsive noise\, with a focus on extending bias-compensated instrumental-variable methods to contaminated multichannel observations. MVAR models are widely used in time-series prediction\, system identification\, biomedical signal analysis\, and sensor-array processing. However\, conventional estimators can suffer from systematic bias under correlated measurement noise\, and their performance can degrade severely when rare but large-amplitude impulsive samples dominate covariance and correlation statistics. \nThe talk first reviews the transition from AR to MVAR modeling and explains how measurement noise enters the estimation problem. It then introduces the extended instrumental-variable bias-compensation (EIV-BC) framework for correlated noise and discusses why impulsive contamination presents an additional challenge. A robust batch EIV-BC strategy is presented\, using bidirectional estimation and residual-based outlier exclusion to identify unreliable samples before recomputing model statistics. \nExperiments are conducted on both simulated MVAR signals and real-world UCI air-quality sensor data. The results show that impulsive noise significantly degrades standard EIV-BC prediction\, while the robust extension provides more stable estimates and lower prediction errors on non-impulsive test samples. The seminar demonstrates how robust sample selection can improve MVAR estimation reliability in practical noisy sensing environments. \nSpeaker\nMr Mingxi LYU\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMingxi Lyu received the B.S. degree in Mechanical Engineering from Xi’an Jiao tong University in 2019\, and the M.S. degree in Mechanical Engineering from Xi’an Jiao tong University in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His current research interests include multivariate autoregressive regression and statistical robust estimation of chirp signal with their applications. \nOrganiser\nProf. Chan Shing CHOW \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260518/
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:20260519T160000
DTEND;TZID=Asia/Hong_Kong:20260519T170000
DTSTAMP:20260513T121900
CREATED:20260420T064633Z
LAST-MODIFIED:20260420T064633Z
UID:115720-1779206400-1779210000@ece.hku.hk
SUMMARY:RPG Seminar – From Understanding to Intervention: Interpretability-Guided Methods for Improving Large Language Models
DESCRIPTION:  \nAbstract\nLarge language models have achieved impressive performance\, but improving them efficiently and reliably requires more than scaling alone. In this talk\, I present a series of works that explore how internal understanding of LLMs can be translated into practical interventions for better capability\, efficiency\, and controllability. I begin with actionable mechanistic interpretability\, introducing a unified “Locate\, Steer\, and Improve” perspective that turns model analysis into a framework for intervention. I then show how this perspective supports several concrete advances: data-free mixed-precision quantization guided by numerical and structural sensitivity\, multilingual capability enhancement through representation shifting and contrastive alignment\, personalized multi-teacher distillation that routes each prompt to its most suitable teacher\, and coarse-to-fine selective fine-tuning for mitigating catastrophic forgetting while preserving general versatility. Together\, these works reflect a common theme: interpretability is not only a tool for explaining LLMs\, but also a principled basis for designing more efficient training\, compression\, and adaptation methods. \nSpeaker\nMr Hengyuan ZHANG\nDepartment of Electrical and Computer Engineering\nThe University of Hong Kong \nBiography of the Speaker\nHengyuan Zhang is a Ph.D. candidate at the University of Hong Kong\, supervised by Prof. Ngai Wong and Prof. Hayden Kwok-Hay So. His research focuses on the improvement of efficiency and interpretability within large language models. He aims to uncover and characterize the internal processes that govern model behavior\, with the goal of improving model speciality\, interpretability\, and reliability in real-world deployments. He has published multiple papers in leading venues such as ACL\, EMNLP\, TKDD\, and NeurIPS. \nOrganiser\nProf. Ngai WONG \nDepartment of Electrical and Computer Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20260519/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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