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 paper on differentiable Signal Temporal Logic for neurosymbolic AI has been published by LIPIcs
																	21st October 2025
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								New pre-print out on our qualitative study of older adults living with sensors at home
																	15th October 2025
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								Research Assistant in Health Data Science and Innovation
																	2nd October 2025
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								Data Analyst in Health Data Science and Innovation
																	2nd October 2025
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								Alex Hyman offered a studentship to start a PhD in Artificial Intelligence
																	15th August 2025
Some of our Existing and Past External Engagements
 
														 
														 
														 
														 
														 
														 
														 
														 
														 
														 
														 
														
Recent Events
Edinburgh Science Festival: Who wants to live forever?
Life-spans across the world are increasing, but this often overlooks the health-span: the period of life spent healthy and disease-free. So how do we ensure that later life is a healthy life?
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