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the development of Explainable AI Systems that can provide explanations of AI agent decisions to human users. Past work on plan explanations primarily focused on explaining the correctness and validity of plans. In
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discovery. While deep generative models have shown promise in proposing novel molecular structures, they typically require massive, cleanly labelled datasets to train effectively. In practice, acquiring high
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Seizure prediction algorithms will be developed using the one-of-a-kind ultra-long-term human intracranial EEG dataset obtained from the Neurovista Corporation clinical trial of their Seizure
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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This project will investigate and develop the ways in which AI algorithms and practices can be made transparent and explainable for use in law enforcement and judicial applications The Faculty
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privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining
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on developing algorithms to analyse optical signals extracted from video images to obtain vital signs with high accuracy. Key responsibilities include: Robust feature extraction from biomedical signals
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This research focuses on developing and evaluating methodologies for the optimal design of control charts within the framework of Statistical Process Control (SPC). The study aims to determine the
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the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and
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these challenges, calling for the development of responsible AI systems that are transparent, trustworthy, and aligned with human values in educational contexts. This PhD project aims to design, develop, and