Artificial Intelligence
Exploring Artificial Intelligence methods for developing adaptive systems and data-driven solutions to complex real-world challenges.
Focus
Research areas
Machine Learning
Exploring ML techniques to develop intelligent systems, focusing on prediction, pattern recognition, and data-driven solutions.
Deep Learning
Reproducible training, drift detection and the engineering practice that keeps a deployed model honest over time.
Natural Language Processing
Retrieval, summarisation and extraction over domain documents, with a particular interest in evaluating what these systems get wrong.
Computer Vision
Detection and segmentation in settings where labelled data is scarce and the cost of a false negative is high.
Intelligent Diagnosis
Developing intelligent systems to analyze data, identify patterns, and support accurate, timely, and reliable diagnosis.
Publications
6 in total
2026
-
Calibrated Uncertainty Estimates for Clinical Triage Models
Abstract
Placeholder abstract. Triage models are usually evaluated on accuracy alone, which says nothing about whether a confident prediction deserves that confidence. This entry exists so the Research page has something to lay out; replace it with your own.
2025
-
Label-Efficient Defect Detection in Industrial Imaging
Abstract
Placeholder abstract. Annotation budget, not model capacity, is usually the binding constraint in industrial inspection. Replace this entry with your own.