Statistical and Computational Modelling

Statistical and Computational Modelling

Focus of our research

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.

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.

Current projects for this theme:

  • Understanding and predicting the onset of neurological illnesses such as Alzheimer’s and Parkinson’s disease
  • Investigation of the psychological impact of flooding in the UK
  • Spatio-temporal modelling of HIV, Cholera and COVID-19 for propagation, prediction and policy decisions
  • Prediction of pedestrian behaviour to inform connected-vehicle control
  • Modelling of protein dynamics during translocation across cell membranes

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Coventry University No.1 Modern University No.1 Modern University in the Midlands
Coventry University awarded TEF GOLD Teaching Excellence Framework
University of the year shortlisted
QS Five Star Rating 2020
Coventry City of Culture 2021