The Artificial Intelligence Modelling Lab (AIML) engages in a range of theoretical and applied research in Artificial Intelligence (AI) and Machine Learning (ML). Particular areas of interest include interactive theorem proving, formal modelling and verification, machine learning and its combination with higher level symbolic reasoning, as well as its application to healthcare and other complex, real-world domains.

News
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Paper on conformance checking over probabilistic events accepted at HICSS-57
18th August 2023
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Pre-print on Associations between Morbidities in Small But Important Subgroup using a Bayesian approach
8th August 2023
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Paper on brain tumour survival predictions accepted in Computer Methods and Programs in Biomedicine
15th May 2023
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Survey pre-print on Neurosymbolic AI for Reasoning on Graph Structures is out on arXiv
15th February 2023
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Job Opportunity: Research Research Associate in Neurosymbolic AI
3rd December 2022
Some of our Existing and Past External Engagements













Recent Events
Exploring AI Based Approaches for Post-operative Microvascular Free Flap Monitoring
I will discuss my PhD project, which aims to explore AI-based approaches for the postoperative monitoring of microvascular free flaps that are as effective and accurate as clinical assessment at identifying compromised free flaps but which are less subjective.
Speaker: Fiona Smith
The Usage and Improvement of Neurosymbolic AI for Biomedical Applications
My work aims to use and improve neurosymbolic AI to demonstrate how these unique characteristics are especially useful for challenges which are particularly prominent in biomedical data science such as meaningful multimodal data integration for clinical datasets, discovering drug mechanisms of action via long-range dependencies, and few shot learning to predict and understand rare, serious side effects.
Speaker: Lauren DeLong
Predicting in-hospital mortality for ICU patients with liver disease using process mining and deep learning
This research aims to incorporate time series events from the care pathways of ICU patients to enable better survival predictions over time. In this talk, we describe our work on combining Process Mining and Deep Learning and applying it to mortality predictions for ICU patients with liver diseases.
Speaker: Zonglin Ji