Welcome to PEILab 👋

Who We Are

The PolyU Edge Intelligence Laboratory (PEILab), directed by Prof. Song Guo, is developing novel and efficient deep learning architectures, algorithms, and systems over ubiquitous mobile, IoT, and wearable devices. Specific topics of current interest include edge on-device learning, federated learning, and implementation of large-scale deep neural networks in cloud-edge hybrid systems.

Join Us

We are looking for strongly motivated PhD students, Research Assistants, and Postdoctoral Fellows.

Song Guo

Efficient TinyML Systems for Edge Intelligence

Efficient TinyML Systems for Edge Intelligence

In order to conquer the limitations of conventional in-cloud computing, it comes the rise of on-device learning, which makes the end-to-end ML procedure totally on user devices, without unnecessary involvement of the cloud.

Emergency Risk Management in Smart City

Emergency Risk Management in Smart City

With the emergence and drastic improvement of mobile devices (e.g., phones, tablets, drones, and autonomous vehicles), we are now witnessing an exciting revolution of the digital city.

Radiation-free Spine Reconstruction and Posture Analysis Techniques with 3D Imaging

Radiation-free Spine Reconstruction and Posture Analysis Techniques with 3D Imaging

Scoliosis is a sideways curvature of the spine that occurs most often during thegrowth spurt just before puberty. According to the survey and statistics of China Child Development Center, more than 20% teens have scoliosis.

New Architectures and Methodologies for High Performance Sharding Blockchain

New Architectures and Methodologies for High Performance Sharding Blockchain

Blockchain draws tremendous attention from academia and industry, since it can provide distributed ledgers with data transparency, integrity, and immutability to untrusted parties for various decentralized applications.

Federated Learning in Resourced Constrained Mobile Edge Network

Federated Learning in Resourced Constrained Mobile Edge Network

Federated learning (FL) has been proposed as a promising solution for future AI applications with strong privacy protection. It enables distributed computing nodes to collaboratively train models without exposing their own data.

  • Mong Man Wai Building, Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR