Skip to content

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.

Some of our Existing and Past External Engagements
Recent Events

In this talk, I will briefly introduce common network science approaches to multimorbidity research, present and explain our methodological approach, and present and show how our method can be easily used by the multimorbidity community via our released software package.
Speaker: Guillermo Romero Moreno

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

Our study aims to utilize various clustering methods to explore connections between pre-existing condition clusters and symptom clusters while preserving their uniqueness. We evaluate different MVC methods—Binary Multi-view Clustering (BMVC), Consensus Graph Learning (CGL), and Bayesian Consensus Clustering (BCC)—against our proposed Bayesian approach.
Speaker: Luwei (Demi) Wang