I have been working closely with my PhD students, Visiting/Internship Students and Visiting Scholars over the years. Please feel free to contact me if you would like to learn more about my work and explore new opportunities.

I am currently looking for PhD students who are interested in Computer Vision, Computer Graphics and Machine Learning. Two potential project directions are listed below and I am open to other relevant topics as well. The candidate is expected to have strong programming skills, some prior experience in machine learning and visual computing (computer vision and/or computer graphics), and good English communication skills. Please contact me for further information. Details for PhD application at the School of Computing Science at the University of Glasgow can be found here.


1. Modelling Close Human-Human and Human-Object Interactions for Human Digitization

The aim of this project is to propose new methods for modelling the close interactions between human-human and human-object. Such an approach can be used for tackling problems in a wide range of tasks, including scene understanding, pose estimation and 3D human reconstruction in Computer Vision, as well as synthesizing interactive contents in Computer Graphics and Virtual Reality.

Analysing the relationships between human-human and human-object from images plays an important role in providing contextual information in addition to the low-level features (such as key points on the human and object). Although encouraging results are demonstrated by using data-driven and deep learning techniques in recent years, handling scenes which contains close interactions between human and objects is still a challenging task since the key entities (human(s) and object(s)) are usually partially occluded and resulted in low-quality input data. In this research, we will bridge this gap by utilising prior knowledge in close interactions to better model the human-human and human-object interactions.

The supervisory team has extensive experience in this area and the details of the relevant publications can be found here.


2. Early Prediction of Cerebral Palsy using Machine Learning and Computer Vision with Multimodal Data

The aim of this project is to propose new machine learning based framework for detecting abnormal infant movement from RGB videos. In particular, this project will focus on modelling the multimodal data collected from our NHS partners to improve the robustness and accuracy of the early prediction of Cerebral Palsy (CP).

CP is the collective term given to a group lifelong neurological conditions and the most prevalent physical disability found in children, with 2.11 diagnoses per 1000 live births. There is also an increased prevalence of CP in infants born prematurely, with 32.4 diagnoses per 1000 infants born very preterm (28-32 weeks gestation), and 70.6 diagnoses per 1000 infants born extremely preterm (<28 weeks gestation).

As such, the early diagnosis of CP is an ongoing area of multidisciplinary research, as it has the potential to allow for early intervention clinical care. However, early diagnosis can be difficult and time-consuming. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve the accessibility of the assessment and also enhance the understanding of the movement development of infants.

The supervisory team has extensive experience in this area and the details of the relevant publications can be found here.


PhD students

Manli Zhu
Northumbria University, United Kingdom (since 2020)
Research interests: Deep Learning, Human Motion Analysis, Healthcare Technology

Shaun Lillie
Northumbria University, United Kingdom (since 2020)
Research interests: VR and AI for Education

Luca Crosato
Northumbria University, United Kingdom (since 2020)
Research interests: Autonomous Vehicles, Reinforcement Learning, Computer Vision

Anthony Ashwin
Northumbria University, United Kingdom (Co-supervised, since 2020)
Research interests: Deep Learning, Active Learning

Cameron Craggs
Northumbria University, United Kingdom (Co-supervised, since 2020)
Research interests: Motion Capture, Virtual Reality

Daniel Organisciak
Northumbria University, United Kingdom (Co-supervised, since 2018)
Research interests: Deep Learning, Computer Vision

Visiting scholars

Aman Goel
Research Intern (since Feb 2022, from International Institute of Information Technology (IIIT), Hyderabad, India)
Research interests: Computer Vision

PhD alumni

Dr. Kevin McCay
Graduated in 2022 - Northumbria University, United Kingdom
Research interests: Computer Vision, Machine Learning, Artificial Intelligence, Healthcare Technology

Dr. Dimitrios Sakkos
Graduated in 2020 - Northumbria University, United Kingdom
Research interests: Deep Learning, Computer Vision, Background Subtraction

Dr. Jingtian Zhang
Graduated in 2020 (Co-supervised) - Northumbria University, United Kingdom
Research interests: Computer Vision, Action Recognition

Dr. Yijun Shen
Graduated in 2019 (Co-supervised) - Northumbria University, United Kingdom
Research interests: Character Animation, Motion Synthesis

Past Contributors

Dr. John Hartley
Post-Doctoral Research Fellow (Northumbria University, United Kingdom)
Research interests: Artificial Intelligence, Drone Controls

Dr. Jacky C. P. Chan
Post-Doctoral Research Fellow (Hong Kong Baptist University)
Research interests: Character Aniamtion, Computer Graphics

Dr. Gary Jiayu Liang
Research Assistant (Hong Kong Baptist University)
Research interests: Computer Vision, Visual Attention, Visual Psychology

Dr. Qianhui Men
Visiting PhD student (City University of Hong Kong, Summer 2019)
Research interests: Deep Learning, Human Motion Synthesis

Kaveen Perera
PhD student (Co-supervised, Northumbria University, United Kingdom)
Research interests: Video Summarization

Chenyang Xia
Research Assistant (Hong Kong Baptist University)
Research interests: Character Aniamtion, Crowd Simulation

Donald C. K. Chan
Research Assistant (Hong Kong Baptist University)
Research interests: Action Recognition