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 domains.
- Callum Abbott’s MSc thesis comes top of MSc in Statistics with Data Science
- Programme Co-ordinator post available on Artificial Intelligence for Multiple Long-Term Conditions Programme
- Mechanisation of Minkowski Spacetime released on the Archive of Formal Proof
- Richard Schmoetten recipient of AFR Grant for his PhD
- Three postdoctoral Research Associate posts available in Artificial Intelligence for Multiple Long-Term Conditions Programme
Some of our External Engagement
Convex optimisation is a subfield of mathematics that studies convex functions and their maxima/minima over a given domain, with applications in control synthesis, signal processing and operations research to mention a few. We describe the how the Lean theorem prover might be used to rigorously check algorithms in the domain, with neural network verification as a potential case study.
Speaker: Ramon Mir Fernandez
In this work, a Relational Graph Attention Network is extended to operate on multimodal biological input and is compared alongside previously established side effect prediction methods to evaluate the efficacy of deep Network Representation Learning for adverse drug reaction prediction.
Speaker: Lauren DeLong
I will give a brief overview of processes and resources, as well as their formalisation in Isabelle/HOL and demonstrate some of its features such as located resources and sensing actions. I will relate our process compositions to proofs in linear logic. Finally I will sum up our future research plans.
Speaker: Filip Smola