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

RPG Seminar – Speculative Jacobi Decoding: Pushing the Limits of Parallelism in Autoregressive Visual Generation
2026-05-27 10:30 - 11:00 (Asia/Hong_Kong)
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.
