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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Our formalisation of Linear Resources and Process Compositions has been published in the Archive of Formal Proof
4th December 2024
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Jiawei Zheng passes his PhD Defence!
29th August 2024
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Our formalisation of Lie Groups and Algebras has been published in the Archive of Formal Proof
2nd August 2024
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Our survey paper on Neurosymbolic AI for reasoning over knowledge graphs has just been published in TNNLS
2nd August 2024
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Our paper “Linear Resources in Isabelle/HOL” has just been published in the Journal of Automated Reasoning
23rd May 2024
Some of our Existing and Past External Engagements
Recent Events
During this talk, I report on progress we have made towards formalising Algebraic Quantum Field Theory (AQFT) and present formalisations in the theories of manifolds, Lie groups, and involutive algebras. I will outline a plan to quickly obtain a minimal formalisation of AQFT, suitable for the study of theorems with physical interpretation.
Speaker: Richard Schmoetten
Predicting brain health outcomes from objectively-assessed sleep duration in UK Biobank
This presentation will presentan assessment of the feasibility of predicting brain health outcomes from sleep duration derived using accelerometery data from UK Biobank.
Speaker: Matt Whelan
Investigating associations between physical multimorbidity and subsequent depression via a systematic cluster analysis
In this talk, we describe our investigation of associations between physical multimorbidity and subsequent depression by performing clustering analysis upon baseline morbidity data for UK Biobank participants and then performing survival analysis to compare time to subsequent depression diagnosis.
Speaker: Lauren DeLong