School of Computer Science and Statistics
In reinforcement learning, agents usually process all inputs equally, even if some are more relevant than others. This project will explore the use of a simplified attention mechanism in a car-following environment, allowing a vehicle to focus more on important neighbours (such as the vehicle directly in front). Students will compare the performance of Multi-Agent … Read more
Reinforcement learning agents often behave conservatively or inconsistently at the start of training, leading to the so-called cold-start problem. This project will simulate a simple traffic scenario and train a multi-agent RL-based autonomous vehicle controller. Students will then explore strategies to reduce cold-start effects, such as imitation learning for initialization or reusing past experiences, and … Read more
Drying clothes outdoors is one of the most affordable and environmentally friendly methods, but in Ireland’s unpredictable weather, laundry that is nearly dry can quickly become wet again. This project will focus on the design and development of a smart laundry drying assistant that helps users make better decisions about when to dry clothes outside. … Read more
Before fully autonomous driving is achieved, connected autonomous vehicles (CAVs) will operate for a certain period in mixed traffic, which includes both CAVs and human-driven vehicles (HDVs). The dynamic and interactive conditions in mixed traffic scenarios renders CAV decision making particularly challenging. This project will investigate the use of Graph Convolutional Deep Reinforcement Learning for … Read more
This project will explore applications of Reinforcement Learning (RL) for a real-world application in the railway domain. To address increasing demand, railway providers aim to increase traffic density on the existing network. However, dense traffic can result in delayed trains and infrastructure disruptions, which can impact planned trips in a large part of the network … Read more
I am happy to supervise a project in all the areas of computer networking that I am interested in — wireless networking, Machine Learning, Artificial Intelligence and in their application to a wide range of real world settings (healthcare, IoT, structural health monitoring). I am particularly interested in exploring some aspects of quantum computing; for … Read more
The performance of artificial intelligence based learning models is often initially assessed using well established benchmark datasets. However, satisfactory performance on these datasets does not guarantee similar performance in real-world settings where the data may be significantly more imbalanced. For example, in a medical dataset a positive diagnosis may be a relatively rare event and … Read more
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Concept inventories are research-based multiple-choice tests that are used in educational settings to measure a student’s knowledge of a set of concepts while also capturing conceptions and misconceptions they may have about the topic under consideration. They provide useful information for students, lecturers and educational researchers. For example, they can provide lecturers with a measure … Read more
Federated learning is used in distributed collaborative networks where multiple clients coordinate to train AI models without the need to share raw data. This is advantageous in a number of settings; for example; in health care where there are privacy/ethical issues associated with sharing data across multiple sites and in IoT networks where the sharing … Read more