Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系

RPG Seminar – Toward Trustworthy Network Intrusion Detection: From Representation Learning to Multi-Agent Orchestration
2026-04-27 10:30 - 11:30 (Asia/Hong_Kong)
Zoom Link:
https://hku.zoom.us/j/94349548162?pwd=[REDACTED]
Abstract
Modern network intrusion detection systems face critical challenges in open and dynamic environments, including concept drift as threat patterns evolve, label noise as annotations are inherently unreliable, and limited interpretability as detection decisions are difficult to understand and trust. This seminar presents our recent progress toward a trustworthy intrusion detection paradigm. We propose a representation enhancement framework that learns drift-aware and noise-robust feature embeddings, enabling detectors to sustain high accuracy under non-stationary and imperfectly labeled conditions. We further introduce an LLM-based hierarchical multi-agent system that brings semantic reasoning into intrusion detection, delivering both strong detection performance and human-interpretable explanations. These works chart a path from robust representation learning to next-generation agentic inference, advancing network intrusion detection toward greater robustness, adaptivity, and trustworthiness.
Speaker
Mr Shuo YANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Shuo Yang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at The University of Hong Kong, under the supervision of Prof. Edith C. H. Ngai. His current research interests include Network Security, Trustworthy AI, Data Mining and LLM Agent.
Organiser
Prof Edith C. H. NGAI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Mobile Reasoning-as-a-Service via Distributed LLM Inference-Time Scaling
2026-04-27 14:30 - 15:30
Zoom Link:
https://hku.zoom.us/j/6589092185?pwd=[REDACTED]
Abstract
Inference-time scaling has emerged as an effective approach for enhancing the capabilities of Large Language Models (LLMs), addressing the growing demand for stronger reasoning without increasing model size. This novel form of LLM scaling comprises two representative approaches: explicit reasoning, which generates intermediate chain-of-thought tokens during an explicit thinking phase, and implicit reasoning, which iteratively updates hidden states in the latent space without producing explicit outputs. Despite their effectiveness, both paradigms incur substantial computational and memory overhead, raising challenges for deployment on resource-constrained edge devices. To address these issues, we propose a Mobile Reasoning-as-a-Service framework that treats reasoning as a computational service accessible to edge devices over wireless networks. Focusing on implicit reasoning, we leverage its recursive structure to partition hidden-state updates between edge devices and servers, enabling cooperative inference that allows devices to access additional cloud computation on demand. To handle dynamic wireless conditions and optimize long-term performance, we formulate a joint computation and communication scheduling problem and solve it using a semantic Mixture-of-Experts (MoE)-based Soft Actor-Critic (SAC) algorithm to address heterogeneity in wireless conditions and task demands. Ultimately, this work validates distributed inference-time scaling through semantic-aware collaborative reasoning services, offering a scalable and efficient paradigm for deploying advanced LLM reasoning at the mobile edge
Speaker
Mr. Guanchen LIU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Guanchen Liu received his B.S. degree from the Harbin Institute of Technology. He is currently an MPhil candidate in the Department of Electrical and Computer Engineering at the University of Hong Kong, with a research focus on LLM reasoning.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Intrinsic Wavelength Tuning in InGaN/GaN MQWs Nano/MicroLEDs
2026-04-29 16:00 - 17:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Intrinsic wavelength tuning in InGaN/GaN multiple quantum wells (MQWs) is a critical aspect of optimizing their performance for various applications, including display technologies and visible light communication. This project investigates the underlying mechanisms of wavelength tuning in InGaN/GaN MQWs and explores strategies to achieve precise control over the emission wavelength. Through a combination of experimental studies and theoretical modeling, we analyze the effects of piezoelectric strain, indium composition, and external electric field on the emission properties of nano/microLEDs. The findings provide insights into the design and fabrication of LEDs with tuable emission characteristics, contributing to advancements in optoelectronic device engineering.
Speaker
Mr. Bo LU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Bo LU received his BSc degree in Physics from the China University of Mining and Technology in 2018 and MSc from the Southern University of Science and Technology (HIT joint program). He is currently pursuing a PhD in the Department of Electrical and Computer Engineering at the University of Hong Kong. His research focuses on the fabrication, characterization, and applications of long-wavelength InGaN/GaN MQWs microLED, particularly in wavelength tuning engineering.
Organiser
Professor Anthony Hoi Wai CHOI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Accuracy-Oriented Resource Scheduling for Satellite-Air-Ground Sensing, Computing, and Communication Systems
2026-05-05 14:00 - 15:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
With the evolution of 6G mobile communications, achieving 99.999% seamless global coverage has become a key requirement. Traditional terrestrial networks still suffer from coverage blind spots in scenarios such as emergency rescue and vast ocean areas. Therefore, building a space–air–ground integrated network by combining satellite, aerial, and terrestrial systems has become an important trend. This seminar focuses on mission-driven target recognition within the integrated sensing, communication, and computing (ISCC) framework, emphasizing efficient end-to-end system design under physical constraints. It analyzes the theoretical lower bounds of multiclass classification error probability, including discrimination gain and feature distribution variance, and discusses multi-objective trade-offs and optimization frameworks. The seminar also highlights future research on user-oriented intelligent scheduling for space–air–ground collaborative resources, with the goal of reducing end-to-end latency, improving resource allocation efficiency, and enhancing overall system performance.
Speaker
Mr Zhiyuan XU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Xu Zhiyuan received the B.Sc. degree from Sun Yat-sen University, Guangzhou, China, in 2023. He is currently working toward the M.Phil. degree with The University of Hong Kong, Hong Kong. His research interests include 6G communications.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Novel Two-Dimensional (2D) Memory Devices: From Material Innovation to Functional Integration
2026-05-08 14:30 - 15:30
Zoom Link
https://hku.zoom.us/j/92285676627?pwd=[REDACTED]
Abstract
During the period of the Internet of Things (IoT) and big data, data capacity is growing exponentially, the delay and loss caused by data transmission make the traditional von Neumann computing architecture urgently need to be overturned and restructured. This has led to a growing demand for emerging memory devices, posing significant challenges to conventional silicon-based memory technologies. In recent years, two-dimensional (2D) materials leveraging their unique physical properties, ultra-thin thickness and no dangling bonds have been wide-ranging used in the fabrication of various electronic and optoelectronic memory devices. In this seminar, we first briefly introduce several mainstream types of 2D memory devices developed in recent years and their working mechanisms. We then propose a 2D infrared-sensing memory device (ISMD) based on the Se0.3Te0.7/CuInP2S6 (CIPS) heterostructure, where Se0.3Te0.7 serves as the channel and CIPS functions as the ferroelectric auxiliary layer. The coupling between interfacial defect trapping and ferroelectric polarization endows the device with non-volatile multi-bit memory capability programmable by electrical pulses. Meanwhile, the device exhibits a transient infrared response at 1550 nm, with its responsivity negatively correlated to the channel conductance. By integrating sensing, memory, and computing in a single device, this work broadens the research scope of 2D memory devices.
Speaker
Mr. Xuyang ZHENG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Xuyang Zheng is an MPhil student in the Department of Electrical and Computer Engineering at The University of Hong Kong, supervised by Prof. Can Li. He received his B.S. degree in Functional Materials from South China University of Technology (SCUT) in 2024. His research interests include 2D memory devices for neuromorphic computing.
Organiser
Prof. Can LI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Day–Night Mechanism-Aware Causal Modeling for Wind Power Forecasting: A Physics-Guided NLSEM Framework
2026-05-08 16:00 - 17:00
Zoom Link
https://hku.zoom.us/j/95263844640
Abstract
Wind power forecasting remains highly challenging due to the strong nonlinearity of atmospheric dynamics,pronounced diurnal regime differences, and substantial uncertainties in multi-source meteorological data. Conventional black-box machine-learning models mainly rely on observational correlations, often neglecting physical constraints and causal mechanisms, which leads to limited interpretability and poor robustness under distribution shifts. In this seminar, we proposes a unified forecasting framework that integrates physics-constrained data construction, multi-site causal structure learning, and a global nonlinear structural equation model (NLSEM). The framework combines full-variable Granger causality networks with PCMCI+ to identify distinct day and night-time causal directed acyclic graphs (DAGs). Within the NLSEM, physical monotonicity, environmental invariance, and counterfactual-consistency regularization are explicitly enforced. The resulting model supports causal inference through dointervention analysis, ATE/CATE estimation, and counterfactual reasoning. Experiments conducted on three coastal wind farms demonstrate consistent performance improvements over strong machine-learning baselines, while revealing physically meaningful causal drivers of wind-power generation.
Speaker
Mr. Yuxuan WANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Yuxuan Wang received the B.S. degree in New Energy Science and Engineering from Huazhong University of Science and Technology, and the M.S. degree in electrical engineering from University of Leeds. 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 wind power forecasting, causal inference, and nonlinear modeling for renewable energy systems.
Organiser
Prof. Yunhe HOU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – HOPE for Quantitative Photoacoustic Microscopy of Lipids: From Acquisition to Analysis
2026-05-12 16:00 - 17:00
Zoom Link
https://hku.zoom.us/j/7489542129?pwd=[REDACTED]
Abstract
Photoacoustic microscopy (PAM) in the 1.7-μm absorption window has emerged as a promising modality for label-free lipid imaging due to the strong overtone absorption of C—H bonds at this wavelength. However, quantitative lipid imaging is constrained by the performance of existing nanosecond laser sources and depth-dependent resolution degradation caused by optical scattering. In this seminar, we present a full-stack optical-resolution PAM platform for the quantitative imaging and analysis of lipids in biological tissues. The key constituent of this platform is a novel 1725-nm hybrid optical parametric oscillator emitter (HOPE) source tailored for PAM applications. The laser source provides superior spectral energy density, narrow bandwidth, and high optical signal-to-noise ratio, allowing for high-sensitivity mapping of lipids in tissues. Using this system, we demonstrate clinically relevant differentiation of hepatic steatosis levels in ex vivo human liver specimen, with demonstrated potential as a diagnostic tool for metabolic dysfunction-associated steatotic liver disease (MASLD). We further develop a physics-informed deconvolution framework incorporating to address scattering-induced resolution degradation in three-dimensional PAM images. The framework achieves substantial resolution recovery of up to approximately twofolds in out-of-focus regimes and is additionally applied to the volumetric visualisation of laser-induced thermal ablation lesions in human liver tissue. Together, these developments establish a comprehensive 1.7-μm PAM framework for high-sensitivity lipid imaging and advanced volumetric tissue characterisation.
Speaker
Ms Najia SHARMIN
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Ms. Najia Sharmin is a PhD candidate in the Photonic Systems Research Lab at The University of Hong Kong under the Hong Kong PhD Fellowship Scheme and the HKU Presidential PhD Scholar Programme. Her research focuses on advancing photoacoustic microscopy for biomedical applications, combining laser system design, optical modeling, and image processing to improve diagnostic and translational capabilities. Her PhD work is particularly centered on non-invasive, label-free detection of lipid distributions in human liver tissues in the 1.7-µm optical window.
Organiser
Prof Kenneth K.Y. Wong
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Towards Controllable and Cinematic Visual Content Generation
2026-05-13 10:00 - 11:00
Zoom Link
https://hku.zoom.us/j/96029601263
Abstract
This seminar presents novel solutions to bridge the gap between creative intent and automated visual synthesis. We first address the challenge of compositional alignment in text-to-image generation. This includes T2I-CompBench(++) for systematic evaluation of multi-attribute prompts, and GenMAC, a multi-agent collaborative framework that enables precise semantic orchestration. Second, we target the lack of cinematic artistry in video generation via Filmaster, a system that injects professional camera language and rhythm into the synthesis process using real-film references. Finally, we tackle spatio-temporal inconsistency in dynamic sequences using CineScene, which leverages implicit 3D-aware representations to preserve scene consistency across camera trajectories. Together, these contributions pave the way for next-generation controllable tools in digital cinematography and professional content creation.
Speaker
Ms Kaiyi HUANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Kaiyi Huang received her BSc degree and MSc in Electrical Engineering from the Shanghai Jiao Tong University, and a double MSc degree from Ecole Centrale Paris (joint program). She is currently pursuing a PhD in the Department of Electrical and Computer Engineering at the University of Hong Kong. Her research focuses on the image/video generation, film generation and world model.
Organiser
Prof Xihui LIU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

Seminar on Terahertz Needle Beam Forming Transceiver Systems Based on Advanced Packaging and Massive Chiplet Integration
2026-05-14 09:30 - 10:30
Zoom Link
https://hku.zoom.us/j/98399262138
Abstract
Future sensing and communication applications are converging toward a common technical objective: the development of miniaturized, integrated, reconfigurable, high-speed, and high-precision wireless hardware. These specific requirements fundamentally push the frequency of wireless systems into sub-THz regime, where both large absolute bandwidth and electrically large apertures can be achieved within a compact physical size. In this talk, three hardware solutions are presented, targeting sub-THz needle beam forming transceiver systems based on advanced packaging and massive chiplet integration. First, a 140-GHz monostatic SoC transceiver is demonstrated, which features a low-inherent-loss full-duplexer with self-interference cancellation, effectively mitigating beam-misalignment issues when interfacing with large THz EM apertures. Second, to explore the feasibility of >200GHz passive antennas and interconnects within low-cost organic packages, another 240-GHz SiP transceiver tailored for cross-polarimetric radar sensing is developed. Finally, implementing the 240-GHz SiP transceiver as the spatial feed, a 240-GHz 6400-element aperiodic reflectarray consists of 25 organic package modules and 400 CMOS chiplet integration is presented, which saves 93% of the silicon area with a given radiating aperture. The full system demonstrates <0.9° beamwidth with ±50° 2D beam steering range, and high-resolution near-field imaging. These hardware solutions and prototyping considerations provide a practical pathway toward large-scale, distributed and reconfigurable sub-THz wireless hardware.
Speaker
Dr. Xibi CHEN
Doctor of Philosophy,
Massachusetts Institute of Technology (MIT), Cambridge, MA
Speaker’s Biography
Xibi CHEN received his Ph.D. degree from the Department of Electrical Engineering and Computer Science (EECS), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2026. He received his B.S. and M.S. degrees from Tsinghua University, Beijing, China, in 2017 and 2020, respectively. From 2015 to 2017, he was a Research Assistant with the Microwave and Antenna Institute, Department of Electronic Engineering, Tsinghua University. He later became a Graduate Student Researcher in the same institute from 2017 to 2020. From 2020 to 2026, he was a Graduate Student Researcher at EECS, MIT. His research background includes terahertz (THz) integrated circuits and systems, electromagnetics, advanced packaging technologies, large-scale phased arrays, radar sensing, and high-speed communications. He worked in Texas Instruments (Kilby Lab) and Intel Corporation as Summer Research Interns in 2023 and 2024, respectively. Dr. Chen was the recipient of 2025-2026 IEEE SSCS Predoctoral Achievement Award, and 2024 IEEE MTT-S Tom Brazil Graduate Fellowship. He also received 2025 MIT EECS MathWorks Fellowship Award, ISSCC 2022 Student Travel Grant Award, and Analog Devices Outstanding Student Designer Award.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Electronic Engineering,
The University of Hong Kong

RPG Seminar – HIMSA: A Heterogeneous In-Memory Computing and Searching Architecture for Efficient Attention-Based Models
2026-05-14 10:30 - 11:30
Zoom Link
https://hku.zoom.us/j/99174148480?pwd=[REDACTED]
Abstract
The Transformer architecture, the foundation for modern large language models (LLMs), has revolutionized natural language processing and other AI domains. However, its significant computational and memory requirements, primarily from the matrix multiplication in the self-attention mechanism, present major challenges for conventional hardware. While intensive research on in-memory computing (IMC) technology offers a path to overcome the memory bottleneck, using IMC for Transformers remains challenging. This is because the dynamic matrix multiplication with frequently changing Key, Query, and Value matrices require frequent and costly write operations that are ill-suited for non-volatile memories (NVM) technologies like ReRAM. This work introduces HIMSA Heterogeneous In-Memory Computing and Searching Architecture, which employs vector quantization on K and V matrices. This technique transforms the dynamic vector-matrix multiplications into static operations performed on pre-trained codebooks, thereby eliminating the need for runtime write operations in the attention mechanism. The proposed architecture was evaluated through circuit-level simulations that account for the peripheral circuit designs, including nearest neighbor search and the division-less Softmax operations. More importantly, its write-free attention mechanism mitigates the concerns over limited write endurance of ReRAM devices . This work presents a promising pathway toward highly efficient NVM-based hardware acceleration for next-generation AI models.
Speaker
Mr Muyuan PENG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Peng Muyuan received the B.S. degree at the University of Science and Technology of China majored in Applied Physics. 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 non-volatile memory devices, in-memory computing and related neural network accelerations.
Organiser
Professor Can LI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Cooperative Edge AI: From Event-triggered Inference to Efficient Model Downloading
2026-05-14 13:30 - 14:30
Zoom Link
http://hku.zoom.us/j/7074144117?omn=95813783034
Abstract
Cooperative edge AI enables edge devices and edge servers to collaboratively execute intelligent tasks under limited computation, storage, energy, and communication resources. In this talk, we discuss two complementary research directions toward communication-efficient cooperative edge AI. First, we introduce an event-triggered cooperative inference framework for rare-event detection in edge intelligence systems. Rare events are usually infrequent but highly critical, while conventional edge inference systems may overlook them due to data imbalance and rigid resource allocation. To address this issue, a dual-threshold multi-exit architecture is adopted, allowing confident normal events to be processed locally while complex or uncertain rare events are selectively offloaded to the edge server for more accurate classification. Second, we present an efficient AI model downloading framework based on parametric-sensitivity-aware retransmission. Instead of treating all model parameters equally, this framework exploits the unequal importance of neural network parameters and allocates wireless retransmission resources to more sensitive model packets. In this way, downloading latency can be reduced while inference performance is preserved. The talk concludes with a discussion of future research directions in cooperative edge AI, highlighting open challenges and opportunities in communication-efficient inference, adaptive model deployment, and resource-aware edge intelligence.
Speaker
Mr Zhou You
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Zhou You is currently pursuing a Ph.D. degree in the Department of Electrical and Electronic Engineering at The University of Hong Kong, under the supervision of Prof. Kaibin Huang. He received his B.Eng. degree in Electrical Engineering from the University of Wisconsin–Madison, USA, in 2021. His research interests include wireless communications, edge inference, and AI model downloading.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Stabilizing Streaming Video Geometry via Dynamic Feature Normalization
2026-05-15 14:00 - 15:00
Zoom Link
https://hku.zoom.us/j/94833409754?pwd=[REDACTED]
Abstract
Consistent 3D geometry estimation from streaming RGB input is crucial for real-world applications such as autonomous driving, embodied AI, and large-scale reconstruction.
While modern monocular geometry foundation models achieve strong single-image accuracy, they exhibit severe temporal inconsistency on continuous input, notably dominated by scale–shift drifting. Through targeted empirical analysis, we trace this instability to its root cause: fluctuations in latent feature statistics, whose mean and variance directly determine the predicted depth’s scale and shift. Building on this insight, we introduce Dynamic Feature Normalization (DyFN), a lightweight, causal recurrent module that dynamically and robustly modulates feature statistics to maintain stable geometry over time. We adapt powerful pretrained monocular geometry models for streaming by finetuning only DyFN, a mere 2% additional parameters, while keeping the backbone frozen, thereby achieving temporal consistency without compromising single-image accuracy. Extensive experiments across four benchmarks show that DyFN effectively eliminates temporal artifacts such as disjointed layering and positional jitter, and achieves state-of-the-art temporal stability, improving over prior streaming methods by up to 14% and even outperforming heavier non-causal video baselines.
Speaker
Mr Xiaoyang LYU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Xiaoyang Lyu is a fourth-year PhD student in the CVMI Lab at the University of Hong Kong, where he is supervised by Prof. Xiaojuan Qi. He holds a Master’s degree from Zhejiang University and a Bachelor’s degree from the Harbin Institute of Technology.
Xiaoyang’s research focuses on bridging the gap between physical and digital environments by replicating complex physics, geometry, and material properties within simulators. He is driven by the conviction that high-fidelity world modeling is essential for advancing embodied AI and developing agents that can effectively assist in the real world.
Organiser
Prof Xiaojuan QI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Unveiling the Relationship Between Cation Content and Zeta Potential of Colloids for Forming High-Quality Perovskites
2026-05-15 16:00 - 17:00
Zoom Link
https://hku.zoom.us/j/94017530661
Abstract
There 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.
Speaker
Mr. Qi XIONG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Qi 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).
Organiser
Prof. Wallace C.H. CHOY
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Robust Multivariate Autoregressive Model Estimation under Impulsive Noise
2026-05-18 16:00 - 17:00
Zoom Link
https://hku.zoom.us/j/9995553636?omn=96185323037
Abstract
This 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.
The 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.
Experiments 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.
Speaker
Mr Mingxi LYU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Mingxi 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.
Organiser
Prof. Shing Chow CHAN
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Toward World Models that are Consistent, Physically-Grounded, and Causal
2026-05-19 11:00 - 12:00
Zoom Link
https://hku.zoom.us/j/98470480541?pwd=[REDACTED]
Abstract
World models that simulate future states from past observations are increasingly central to video generation and embodied AI. Yet most existing approaches fall short along two fundamental axes: maintaining temporal consistency over long rollouts, and grounding spatial reasoning in 3D geometry.
This talk presents two research projects addressing each of these challenges, working toward world models that are both temporally consistent and physically grounded — two properties we argue are essential prerequisites for tackling causality and action understanding.
Speaker
Miss Xiaoshan WU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Xiaoshan Wu is a third-year PhD student in the CVMI Lab at the University of Hong Kong, where she is supervised by Prof. Xiaojuan Qi.
Xiaoshan Wu’s research focuses on long-horizon, 3D-aware world models, with an emphasis on temporal consistency and physical grounding.
Organiser
Prof Xiaojuan QI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Foundation-style Methods for Real-Time Statistical Dependency Measurement and Its Applications
2026-05-20 11:00 - 12:00
Zoom Link
https://hku.zoom.us/j/9021481973?omn=94827890905
Abstract
Mutual information has long served as a principled measure of statistical dependence, but computing it from empirical samples is notoriously difficult: neural estimators rely on costly gradient-based optimization for every new dataset, which limits their use inside real-time and large-scale pipelines. This talk presents a framework that pretrains a neural estimator on a synthetic meta-distribution and then evaluates any new distribution in a single forward pass — turning a per-dataset optimization problem into an inference problem. Cheap, online dependency readings further act as many applications, including live training diagnostics, and as a regularization signal for detecting key frames.
Speaker
Mr. Zhengyang HU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Zhengyang Hu, a three-year PhD in ECE, mainly focuses on statistical dependency measurement and foundational-style data science models.
Organiser
Prof Yanchao YANG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Dual Gate Phototransistors: Mechanism, Performance Optimization, and Application in SWIR Detection
2026-05-20 14:30 - 15:30
Zoom Link
https://hku.zoom.us/j/8795454775?omn=92351306157
Abstract
Dual‑gate phototransistors enable independent control of carrier accumulation and separation within a single channel, offering high gain, low noise, and tunable responsivity. To start, this seminar reviews their fundamental mechanisms, optimization strategies, application in SWIR photodetection and novel applications. We then present a graphene/h‑BN/Se0.3Te0.7 dual‑gate phototransistor for short‑wave infrared (SWIR) detection. Graphene serves as top gate electrode, h-BN is the top gate dielectric and Se0.3Te0.7 serves as the p-type channel. Electrical measurements reveal p‑type behavior, with optimal on‑state current achieved under negative dual‑gate bias. Surprisingly, under 1550 nm SWIR illumination, the highest photoresponse occurs under positive dual‑gate bias. This opposite polarity dependence indicates that photoconductive gain is dominated by efficient collection of photogenerated carriers. The h‑BN layer suppresses dark current while providing a strong vertical field. Our results demonstrate that this heterostructure, combined with dual‑gate modulation, offers a scalable route for high‑performance, low‑cost SWIR photodetection.
Speaker
Mr ZHOU Bufan
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Bufan Zhou is an Mphil student in the Department of Electrical and Computer Engineering at the University of Hong Kong, supervised by Prof. Can Li. He received his BSc. degree in Engineering Physics from the Hong Kong Polytechnic University in 2024. His research interests include 2D dual-gate phototransistors.
Organiser
Prof. Can LI
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – From Understanding to Intervention: Interpretability-Guided Methods for Improving Large Language Models
2026-05-21 16:00 - 17:00 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
Abstract
Large 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.
Speaker
Mr Hengyuan ZHANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Hengyuan 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.
Organiser
Prof. Ngai WONG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Can Image Models Think? Benchmarking and Empowering Models with Knowledge and Reasoning
2026-05-27 09:30 - 10:30
Zoom Link
https://hku.zoom.us/meetings/95832932074/invitations?signature=SCg4igomh110T7WUpW6op2xeFgb9WgU92xDTYI2sp8s
Abstract
Text-to-image generation models have achieved impressive visual quality, yet they largely fail when prompts require reasoning or world knowledge to interpret. Generating an image of “a park thirty minutes after heavy rainfall” or “the city hosting the 2021 Summer Olympics” demands inference and domain knowledge, not just pattern matching. This seminar examines the gap between what current T2I models can generate and what they can genuinely understand, exploring how reasoning can be evaluated, measured, and ultimately integrated into the image generation process.
Speaker
Miss Kaiyue SUN
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
I am a PhD student at the Department of Electrical and Computer Engineering, University of Hong Kong, supervised by Prof. Xihui Liu. Before joining HKU, I received my bachelor’s degree in Electrical and Electronic Engineering at Imperial College London. My research interests cover deep learning and artificial intelligence, with special emphasis on generative models and unified multimodal models. My publications have been received by NeurIPS, CVPR, ACL, and T-PAMI.
Organiser
Prof. Xihui LIU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Speculative Jacobi Decoding: Pushing the Limits of Parallelism in Autoregressive Visual Generation
2026-05-27 10:30 - 11:00
Zoom Link
https://hku.zoom.us/s/92652052314?pwd=[REDACTED]
Abstract
Autoregressive (AR) models have emerged as a powerful paradigm for text-to-image and text-to-video generation, exhibiting exceptional native in-context learning capabilities and scalable performance. However, their real-world deployment remains severely bottlenecked by the conventional sequential, token-by-token decoding process, which incurs immense inference latency by demanding thousands of sequential forward passes. To break this fundamental sequential dependency, this talk introduces the Speculative Jacobi Decoding (SJD) framework. SJD reformulates the sequential prediction of AR models as a system of non-linear equations, enabling multi-token parallel decoding through a novel probabilistic acceptance criterion that preserves the critical visual diversity required in sampling-based generation. Without requiring any auxiliary networks or model fine-tuning, SJD achieves significant, training-free wall-clock speedups across standard visual benchmarks while maintaining identical generation quality, bridging the gap between powerful AR architectures and efficient real-world deployment.
Speaker
Mr Yao TENG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Yao Teng is currently a PhD student in the Department of Electrical and Electronic Engineering at The University of Hong Kong (HKU), under the supervision of Dr. Xihui Liu. His research interests lie broadly in computer vision, deep generative models, and efficient machine learning inference.
Organiser
Prof Xihui LIU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Multi-Dimensional Nano-Printing of Colloidal Quantum Dots for Infrared Optoelectronics
2026-05-27 14:00 - 15:00
Zoom Link
https://hku.zoom.us/j/94632758376
Abstract
Additive manufacturing enables customised device fabrication for emerging sensing technologies. However, printable (opto)electronic devices with sophisticated architectures, including all-printed photodiodes, face challenges in multi-material and multi-layer printing at micro- and nanoscales with low processing temperatures. Herein, we establish a nano-resolution printing method based on electrohydrodynamic printing (EHDP) to deposit inks from the colloidal nanocrystal (NC) library, followed by in situ room-temperature ligand exchange to functionalise the NC solids. This general approach enables layer-by-layer printing with wide selections of NC inks, ligand reagents, substrates, and device architectures. Chemical-treatment-induced contraction and densification grants printed Ag NC structures electrical conductivity and an achievable feature size and filling ratio of 70 nm and 75%, respectively, constructing wide-gamut structural colour gratings. By exploiting Ag, Au, PbS, and ZnO NCs and compact ligands, we demonstrate all-printed multi-layer infrared photodiodes with sub-10-µm pixel sizes. The nano-printing assembly of hetero-NCs promises the facile integration of multi-functional micro-nano devices.
Speaker
Mr. Zhixuan ZHAO
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Zhixuan Zhao is a fourth-year Ph.D. student in the Department of Electrical and Computer Engineering at the The University of Hong Kong (HKU) under the guidance of Prof. Leo Tianshuo Zhao. He earned a master’s degree in mechanical manufacturing and automation from Northwestern Polytechnical University and a bachelor’s degree from Hefei University of Technology.
Organiser
Prof. Leo Tianshuo ZHAO
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Controllable 3D Content Generation
2026-05-28 10:00 - 11:00
Zoom Link
https://hku.zoom.us/j/3623268282
Abstract
Creating high-quality 3D content requires not only visual realism but also controllability, editability, and structural understanding. This talk focuses on recent progress toward controllable 3D content generation, tracing a path from holistic object-level generation to part-aware modeling. It begins with object-level controllable 3D generation enabled by multi-view conditioning, aiming to guide the overall shape, appearance, and structure of generated objects. The talk then moves toward more fine-grained control, emphasizing the importance of decomposing 3D objects into meaningful parts for downstream applications. Finally, it highlights how part-level representations can support broader goals in 3D generation and embodied AI.
Speaker
Mr Yunhan YANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Yunhan Yang is currently a PhD student in the Department of Electrical and Electronic Engineering at The University of Hong Kong, under the supervision of Dr. Xihui Liu. His research interests lie broadly in computer vision, with special emphasis on 3D vision.
Organiser
Prof Xihui LIU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

RPG Seminar – Embodied Spatial Intelligence for Generalizable Robot Navigation
2026-05-29 10:00 - 11:00
Zoom Link
https://hku.zoom.us/j/97268323750?pwd=[REDACTED]
Abstract
Building embodied agents with spatial intelligence remains a fundamental challenge in robotics and embodied AI. Such agents must understand open-vocabulary semantics, maintain long-term spatial memory, and perform robust real-time navigation in dynamic environments. This talk presents a progression toward generalizable embodied spatial intelligence, spanning open-vocabulary exploration, streaming vision-language navigation, and dual-system navigation architectures that unify high-level reasoning with real-time control.
Speaker
Miss Meng WEI
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Meng Wei is currently a PhD student in the Department of Electrical and Electronic Engineering at The University of Hong Kong, under the supervision of Dr. Xihui Liu. His research interests lie broadly in embodied AI and robot learning, with a particular focus on vision-language navigation, embodied spatial intelligence, and foundation models for long-horizon robotic reasoning and control.
Organiser
Prof Xihui LIU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.

Seminar on Physics-Aware World Model for Video Generation and Embodied AI
2026-06-02 10:30 - 11:30 | Room CB-603, 6/F, Chow Yei Ching Building, The University of Hong Kong
The seminar on “Physics-Aware World Model for Video Generation and Embodied AI” is rescheduled to begin at 10:30 am.
Abstract
In AI and cognitive science, world models are key for planning, reasoning, and learning from experience. An effective world model needs to: sense and learn real-world knowledge, predict and generate real-world scenes, reason and control according to physical laws, and act robustly with human-in-the-loop. Prior work on world models has limited capability in representation/generation and physical awareness. We overcome these limitations through two innovations, and towards the first open-source, physically grounded world model from academia. First, we develop a flow matching and DPO reinforcement learning framework to improve the continuity and physical awareness in world model representation and generation. Our world model PhyWorld achieves simultaneously best-of-the-results in physical awareness and state-of-the-art in open-source video generation. Second, we develop a comprehensive physical awareness benchmarking and arena system. We extract comprehensively 50-60 metrics demonstrating physical law awareness of video generation and trajectory. Our comprehensive benchmark PhyGround includes video quality, common sense, Newtonian mechanics, optics, energy, chemical, materials, etc., which is missing in literature. We generate an agent and a 9B language model for evaluating physical awareness according to these benchmarks. Last, we describe our effort and results towards the concept of “world model for all”, which utilises a single world model for robotic control and robot navigation, task management, planning, task decomposition for high-level management and control, as well as automatic SLAM and 3D reconstruction for environment sensing.
Speaker
Prof. Yanzhi WANG
Professor,
Department of Electrical and Computer Engineering,
Northeastern University
Speaker’s Biography
Yanzhi WANG is a Professor in the Department of Electrical and Computer Engineering and Computer Science at Northeastern University, a senior member of IEEE. His research interests focus on real-time and energy-efficient deep learning and artificial intelligence systems, especially on efficient large language models and large-scale generative AI systems. His research works have been published broadly in (i) machine learning conferences such as AAAI, CVPR, NeurIPS, ICML, ICCV, ICLR, IJCAI, ECCV, KDD, ICRA, ACM MM, ICDM, etc., (ii) architecture and system conferences such as ASPLOS, ISCA, MICRO, HPCA, CCS, VLDB, PLDI, WWW, ICS, PACT, CGO, IPDPS, INFOCOM, ICDCS, DAC, ICCAD, FPGA, FCCM, ISSCC, CICC, RTAS, RTSS, etc., and (iii) IEEE and ACM transactions. His research works have been cited 29,000 times. He has received six Best Paper Awards and another 12 Best Paper Nominations. He has many research awards, including the U.S. Army Young Investigator Award and other young investigator awards, research awards from Google, Intel, Mathworks, etc. His research work has been reported and cited by around 500 media. He has 18 academic descendants as tenure-track faculty members at the University of Minnesota, University of Massachusetts Amherst, University of Arizona, City University of Hong Kong, University of Georgia, Zhejiang University, etc. His alumni have become key contributors in Google Gemini and Alibaba Qwen 3.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Computer Engineering,
The University of Hong Kong

All are welcome!

Seminar on AI-native 6G Architecture: Distributed Intelligence and Programmable Wireless Systems
2026-06-09 10:30 - 11:30 | Tam Wing Fan Innovation Wing Two, G/F, Run Run Shaw Building, The University of Hong Kong
Abstract
This keynote examines the architectural transition from deterministic 5G design to AI-native 6G, where intelligence is embedded across device, RAN, core, operations, and lifecycle layers to meet extreme targets in throughput, latency, positioning, and energy efficiency. Rather than treating AI as an external optimisation layer, the talk presents 6G as a distributed and federated system in which learned policies reshape core engineering, radio control, and spectrum management itself. The presentation then shows how this architectural shift brings together several structural enablers and consequences: network slicing and policy control in the core, AI-assisted beamforming in the RAN, RIS and NTN as programmable extensions of wireless coverage, ISAC as a sensing-capable infrastructure layer, and ONNX/Open RAN style interoperability as part of deployable openness. It also addresses the associated constraints, including inference-driven energy costs, the need for explainable AI and auditability, outcome-based licensing for dynamic spectrum access, and the operational challenge of post-quantum security at telecom scale. The keynote argues that 6G should be understood not as a faster air interface, but as an integrated, programmable, and governable cyber-physical network system whose value and risk emerge from the interaction of its components.
Speaker
Prof. Soumaya CHERKAOUI
Polytechnique Montréal, Canada
Speaker’s Biography
Prof. Soumaya CHERKAOUI is a Full Professor at the Department of Computer and Software Engineering at Polytechnique Montréal, Canada. Her research interests are in wireless networks. She specialises in the convergence of machine learning and communication networks, as well as the application of quantum technologies in future communication systems. Before joining academia as a Professor in 1999, she worked in the industry as a project leader on aerospace-related projects. Pr. Cherkaoui has published over 200 research papers in renowned journals and conferences. She has served as a guest editor and an editorial board member for several prestigious journals, including IEEE Journal on Selected Areas in Communications (JSAC), IEEE Network, IEEE Systems, and Computer Networks (Wiley and Elsevier). Her research has led to successful technology transfers and has been recognised with multiple accolades, including best paper awards, most notably at the IEEE Communications Society’s flagship conference, IEEE ICC 2017, with recent awards at ICCSPA 2024, GIIS 2025, and IEEE LCN 2024. She has played key leadership roles in major conferences, serving as the General Chair of IEEE LCN 2019, Technical Co-Chair of IEEE ICC 2025, and Symposium Co-Chair for IEEE ICC 2018, IEEE Globecom 2018, IEEE Globecom 2015, and IEEE ICC 2014. Additionally, she was Chair of the IEEE Communications Society’s Technical Committee on IoT, Ad Hoc, and Sensor Networks (2020–2021), the second-largest technical committee within IEEE ComSoc. Prof. Cherkaoui is the recipient of the C.C. Gotlieb Computer Award, the Mirela Notere Award and the IEEE Bio-Inspired Computing STC Leadership Award and was recognised as a “Star” in Networking and Communications by N2Women. She is also an IEEE Communications Society Distinguished Lecturer.
Organiser
Prof. Edith Cheuk Han NGAI
Department of Electrical and Computer Engineering,
The University of Hong Kong
Supported By
Tam Wing Fan Innovation Wing Two

All are welcome!

RPG Seminar – Adaptive Lightweight Learning for Edge Non-Intrusive Load Monitoring
2026-06-17 11:00 - 12:00
Zoom Link
https://us05web.zoom.us/j/88334748156?pwd=[REDACTED]
Abstract
Non-intrusive load monitoring provides appliance-level electricity consumption information from aggregate household measurements, supporting residential energy management without installing sensors on individual appliances. Processing these measurements on local edge devices can provide timely feedback while preserving user privacy. However, practical deployment remains difficult because edge devices have limited computing and memory resources, and differences among households can reduce the accuracy of models trained elsewhere. This seminar presents a lightweight learning framework that enables non-intrusive load monitoring models to operate on resource-constrained devices and adapt to previously unseen households without requiring appliance-level labels from local users. The study examines the full process from preparing a model for edge deployment to improving its performance after deployment. Ultimately, the framework enables accurate, low-cost, and privacy-preserving household energy monitoring across diverse edge devices and residential environments.
Speaker
Mr Taoyu LU
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Taoyu LU received the B.Eng. degree in electrical engineering and automation from Huazhong University of Science and Technology in 2024. He is currently pursuing the M.Phil. degree in electrical and electronic engineering at the University of Hong Kong. His current research interests include low-carbon energy systems and edge intelligence in smart grids.
Organiser
Prof. Yi WANG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.
