Subject:Machine Learning
Source: https://projects.scss.tcd.ie/subject_area/machine-learning/ Parent: https://projects.scss.tcd.ie
Machine learning models are increasingly used to predict time-to-event outcomes — for example, how long a patient might survive after treatment or when a machine is likely to fail. Unlike standard predictions, these models produce survival curves, which change over time. Existing explanation tools like Shapley values can tell us which features matter, but they … Read more
I am happy to supervise final-year undergraduate projects and master’s theses (Data Science). Below are the research topics. As these topics rely on advanced mathematical and statistical methods, only candidates with a strong background in mathematics or statistics will be considered. I also welcome PhD enquiries. If you’re interested, please contact me to discuss your … Read more
Risk detection models (such as IBM’s Granite Guardian) are increasingly used to flag harmful prompts and responses in large language model pipelines. These systems are trained on human and synthetic data to identify risks across multiple dimensions, but their reliability and fairness are not guaranteed. They may over-flag certain groups, miss subtle harms, or be … Read more
This project investigates how post-hoc counterfactual explanations can be used to debug opaque models such as deep neural networks by revealing which feature changes most influence predictions. In applications like anomaly detection, counterfactuals help clarify why certain cases are flagged as abnormal and expose when models rely on spurious correlations or biased patterns. By using … Read more
Project Description: Optical fibre networks are critical to global connectivity, yet they are susceptible to accidental damage or deliberate tampering. While distributed acoustic sensing (DAS) has been widely investigated for disturbance detection, an alternative approach relies on monitoring the state of polarisation (SOP) of light propagating in the fibre. The SOP is highly sensitive to … Read more
Project Description: Recent advances in distributed fibre sensing have shown that existing optical fibre cables, originally deployed for telecommunications, can also serve as dense sensor arrays. By sending probe signals through the fibre and analysing the backscattered light, it is possible to extract information about environmental changes such as vibrations, temperature, and strain along the … Read more
Are the connections between the Star Wars characters more than meets the eye? In this project, data on interactions between Star Wars characters (from an episode of the students choosing) are studied through the lens of latent space models. This model can construct a latent social space that reveals the underlying structure of this iconic … Read more
In the last decade, Airbnb has become a mainstay for tourists worldwide. In this project, we will use data from insideairbnb.com to obtain a better geographic understanding of Dublin. Which parts are expensive or reasonable to rent? Where do tourists like to stay, and in what type of accommodation? We will study these issues using … Read more
Forecasting conflict on a fine-grained grid level has real-life policy implications that can empirically inform meaningful healthcare and peace-preservation decisions. In many settings, interpretable models have the appeal that policymakers know how to draw conclusions from the model and do not have to base their decisions on black-box models. On the other hand, machine learning … Read more
This project focuses on the rich field of algorithmic fairness where the goal is to ensure that predictions are not biased against subgroups of the population whilst maximising predictive performance. One challenge is when we focus on multiple protected attributes.