We work with:
Data fusion aims to develop the mathematical, statistical and algorithmic structures needed to build models of complex phenomena. Integral to this process is ensuring those models are predictive, testable and data centric.
Building predictive and testable data-centric models of complex phenomena in the physical, life and social sciences relies on data fusion – that is, developing fundamental mathematical, statistical and algorithmic infrastructure. Crucial to the process of discovery is the ability to make meaningful inference from observations that are varied in type, volume and precision. The theme will focus on the development of novel probabilistic models and methods to estimate them. These models provide a principled way to fuse information from multiple sources; information from observations, and information from domain specific theories, expert opinion and prior studies.
Having to make important decisions causes headaches for many people. Our team studies the science of decision-making, creating research that will enable everyone from doctors to governments make better decisions.
We study the science of decision-making under uncertainty. Given probabilistic predictions of future events, how do we choose a sequence of decisions to maximise a particular objective? For example, given a dataset with diseases and outcomes of treatments, what treatments should be selected to improve the patient’s recovery? Our research is currently focused on multi-arm bandit settings, Bayesian optimisation and reinforcement learning.