School of Computer Science and Statistics
Anthony Ventresque Large language models can be used as autonomous agents that interact with applications through a browser, potentially allowing tests to be expressed as high-level goals rather than detailed scripts. This project’s vision is to investigate whether such agents can effectively explore and test web applications, and which factors affect their performance. The idea could be … Read more
Automated tests can sometimes become flaky, passing and failing across executions even when the software has not changed, reducing confidence in Software Testing and Continuous Integration. Recent studies have shown that web test flakiness can arise from factors such as asynchronous DOM interactions, timing assumptions, shared state, and test-order dependencies. The aim of this project … Read more
This project will investigate how artificial intelligence, including computer vision, can be used to support safer and more accessible navigation in urban environments. It will explore how information about the surrounding environment, potential hazards and contextual constraints can be combined to assess risk and provide appropriate, explainable assistance to users.
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
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.