Welcome!

About me: I am a Lecturer (Assistant Prof.) working in the field of computer vision for digital health in the School of Engineering Mathematics and Technology, University of Bristol. I am also a visiting researcher at University of Oxford. Before joining Bristol, I was a postdoctoral researcher in Prof. Alison Noble’s group in University of Oxford, working on the projects PULSE and Turing AI WLR Fellowship. I received my Ph.D. degree in the Department of Computer Science from City University of Hong Kong, supervised by Prof. Howard Leung. I was also a visiting student in Prof Hubert P. H. Shum’s and Prof. Edmond S. L. Ho’s groups. I obtained my M.S. degree in the Department of Mathematics from National University of Singapore, and my B.S. degree in the School of Mathematical Sciences from Dalian University of Technology.

PhD Opportunities: I am currently open for PhD applications working on the development of multimodal AI methods for advanced healthcare data analysis. The project will focus on leveraging and integrating various types of imaging data to improve disease monitoring and diagnostic capabilities, with specific applications in monitoring Parkinson’s disease and aiding in computer-assisted diagnosis. The objective is to develop robust, multimodal AI systems tailored for healthcare applications, utilizing diverse data sources to improve patient monitoring, diagnosis, and disease management. The framework will apply computer vision techniques to multimodal data, specifically targeting two key areas: human action and mobility assessment and medical image analysis. 1) Human Pose Estimation and Action Understanding: Utilizing RGB images, video data, 3D skeletal information, and wearable sensors to enable human action analysis. The goal is to develop robust AI models that interpret human movements across various human-centered informatics, with a specific emphasis on indoor monitoring for Parkinson’s disease patients and individuals with mobility difficulties. 2) Medical Image Analysis for Disease Diagnosis: developing AI-driven methods for analyzing the robustness of various medical imaging modalities, such as CT, MRI, and ultrasound. The goal is to support radiologists and clinicians in identifying and diagnosing diseases with greater efficiency and accuracy, thereby enhancing confidence in their assessments.