About
The JC STEM Lab of Smart City is led by Prof. Yuguang "Michael" Fang. It was founded in January 2023 on a fund from Hong Kong Jockey Club Charities Trust. It is currently the home to graduate students, postdoctoral researchers, and visiting scholars.
The Lab focuses on the cutting-edge research in Smart Cities, Internet of Things, Wireless Communications and Networking, Machine Learning, Computer Vision, Privacy and Security, Health Monitoring, and more, exploring how to leverage mobile things to help things, leading to cost-effective and sustainable solutions to building smarter cities.
Research Thrusts
Our Team
Our lab is led by Prof. Yuguang "Michael" Fang, Chair Professor of Internet of Things, Fellow of ACM & IEEE & AAAS, and consists of talented researchers and PhD students working on cutting-edge research in IoT, wireless communications, and smart city technologies.
Our team includes:
- Faculty Members
- Graduate Students and Visiting Researchers
- Former Lab Members
Seminars & Talks
Towards Prevalence of On-Device AI with Full Runtime Adaptability
Speaker:
Prof. Wei Gao (Pittsburgh, USA)
This talk explores adaptable on-device AI for real-time inference and training in resource-limited settings, with applications in healthcare and embodied AI.
Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks
Speaker:
Prof. Dusit Niyato (NTU, Singapore)
This talk explores scalable generative AI through mixture of experts in mobile edge networks, addressing challenges in resource utilization when deployed on local user devices.
Accelerator-Centric Edge AI Architectures for Low-Power and Personalized Wearables
Speaker:
Prof. David Atienza (EPFL, Switzerland)
A talk on edge AI architectures for wearable systems, focusing on integrating different families of accelerators for energy-efficient computing.
Quantum Communications, Applications and Challenges
Speaker:
Prof. Jianqing Liu (NCSU, USA)
An exploration of quantum communications principles, applications such as quantum key distribution, and the technical challenges in the field.
A Decoupled Radio Access Networks Architecture for 6G
Speaker:
Prof. Haibo Zhou (NJU, China)
This talk introduces a fully-decoupled radio access network architecture for 6G that enhances spectrum utilization and improves user experience.
RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity
Speaker:
Prof. Nan Cheng (Xidian, China)
A presentation on a novel distributed learning scheme that integrates federated learning with a model split mechanism to adapt to client heterogeneity.
Stochastic Cumulative DNN Inference for Intelligent IoT Applications
Speaker:
Prof. Weihua Zhuang (Waterloo, Canada)
A discussion on when to offload DNN inference computation from IoT devices to the edge and how to incorporate multiple inference results to improve accuracy.
Data-Driven Anomaly Detection & Prediction for IoT
Speaker:
Prof. Phone Lin (NTU, Taiwan)
This talk illustrates technologies and solutions for anomaly detection/prediction in IoT systems, along with prototypes and applications.
Enhancing Resource Management and Remote 3D Rendering for Future Wireless Networks
Speaker:
Prof. Chih-Wei Huang (NCU, Taiwan)
This talk examines recent advancements in wireless networks for supporting complex applications like MIMO systems and mixed reality experiences.
