Edmond S. L. Ho Ph.D., M.Phil., B.Sc. (Hons.), FHEA

Senior Lecturer
School of Computing Science
University of Glasgow, Scotland, United Kingdom

Address: SAWB 402, Sir Alwyn Williams Building,
University of Glasgow,
Glasgow G12 8RZ,
Scotland, United Kingdom.

Email: Shu-Lim [dot] Ho [at] glasgow [dot] ac [dot] uk
My UoG Webpage

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Edmond S. L. Ho

Edmond Shu-lim Ho is currently a Senior Lecturer in the School of Computing Science (IDA-Section) at the University of Glasgow, Scotland, UK. Prior to joining the University of Glasgow in 2022, he was an Associate Professor in the Department of Computer and Information Sciences at Northumbria University, Newcastle upon Tyne, UK (2016-2022) and a Research Assistant Professor in the Department of Computer Science at Hong Kong Baptist University (2011-2016). He received the BSc degree in Computer Science from the Hong Kong Baptist University, the MPhil degree from the City University of Hong Kong, and the PhD degree from the University of Edinburgh.


His research interests include Computer Graphics, Computer Vision, Biomedical Engineering, and Machine Learning.


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.


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