Complex systems in nature and technology often consist of many simple components, which combine to show complex collective behaviour. These systems require a combination of statistical, mathematical and computational modelling techniques to understand their behaviour. We use a combination of process-driven and data-driven modelling to develop powerful strategies to understand and predict these complex behaviours.<br><br>In addition to data-driven techniques, as embodied by machine learning, we use reverse engineering, as well as mathematical and statistical modelling techniques to analyse real-world problems in the bio-sciences and engineering. Whilst data-driven techniques are described as model-free, they are in fact ways of optimising model parameters, and the quality of the output is only as good as the underlying model.<br><br>Current projects for this theme:<br><br>Understanding and predicting the onset of neurological illnesses such as Alzheimer's and Parkinson's disease<br>Investigation of the psychological impact of flooding in the UK<br>Spatio-temporal modelling of HIV, Cholera and COVID-19 for propagation, prediction and policy decisions<br>Prediction of pedestrian behaviour to inform connected-vehicle control<br>Modelling of protein dynamics during translocation across cell membranes