Anthony Ventresque

Reassembly of archeological artifact fragments requires considerable time and manual effort for researchers along with a necessity to handle potentially fragile material. It would therefore be advantageous for this process to be automatic. In this project we will work on creating a method for matching 3D scans of ancient animal bone fragments in order to … Read more

Sports analytics increasingly relies on machine learning models to understand and predict player behavior, strategy, and outcomes. In team sports like rugby, pose estimation models can extract player keypoints (e.g., body joint locations) from match footage or sensor data, enabling automated performance analysis. However, despite recent progress in pose-based classification tasks (e.g., identifying tackles, passes, … Read more

Sign Language Recognition (SLR) is a field of Computational Linguistics that sits at the intersection of Computer Vision and Natural Language Processing – aiming to effectively extract salient linguistic features from visual data of sign language users. Recent SLR research has centered Machine Learning approaches, however, these methods typically rely on large-scale datasets which are … Read more

The Irish language is considered a low resource language as it has less data available for research, due to it being a minority language. This has implications on technological advancements in areas such as speech recognition, where limited data impacts the development of accurate and accessible digital tools. While transfer learning using multi-lingual models has … Read more

In many sports, coaches and athletes rely on annotated video footage to improve player performance, reduce the risk of injury, and analyse the game. This is also the case for rugby. Stakeholders at all levels, from amateur to professional, invest a lot of time and resources into annotating these games. With video quality and camera … Read more

This project aims at using sports analytics models to optimise decision-making in Fantasy Football. By integrating data from multiple sources, including player performance metrics, historical game data, and injury reports, etc., and using statistical analysis and machine learning algorithms you will predict player success and recommend optimal lineups. 

The overall objective of this project is to explore rugby analytics with a focus on improving player performance and game strategies. The project will involve collecting and analysing data from rugby matches to identify key performance metrics, such as tackling efficiency, player positioning, and team dynamics – to provide insights that could help coaches and … Read more

Sign Language Recognition (SLR) is a field of Computational Linguistics that sits at the intersection of Computer Vision and Natural Language Processing – aiming to effectively extract salient linguistic features from visual data of sign language users. Recent SLR research has centred Machine Learning approaches, however, these methods typically rely on large-scale datasets which are … Read more

Reassembly of archeological artifact fragments requires considerable time and manual effort for researchers along with a necessity to handle potentially fragile material. It would therefore be advantageous for this process to be automatic. In this project we will work on creating a method for matching 3D scans of ancient animal bone fragments in order to … Read more

Manual creation of tests to ensure the quality of software artefacts is a difficult and labour intensive task. Automatic test generation on the other hand (e.g., using a tool like Randoop) usually requires little or no help from the developers — but often ends up creating too many tests of little value. Various techniques have … Read more