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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

Our Postgraduate Researchers (PGRs)

Name Thesis Title DoS Contact
Dominik Klepl Network Inference and Graph Theory: Applications for Characterising Neurodegenerative Diseases from EEG Fei He
Ibnu Febry Kurniawan Internet Of Things-Enabled Contextual Traffic Information Dissemination: Network Design And Performance Optimisation Fei He
James Donelly A Novel Predictive Model for Extreme Climatic Events: Application of Catastrophe Modelling in Evaluating Environmental Hazard and Vulnerability Ali Reza Daneshkhah
Majdi Fanous Enhancing Mangrove Forest Resilience Against Coastal Degradation and Climate Change Impacts Using Advanced Bayesian Machine Learning Methods Ali Reza Daneshkhah
Shenal Gunawardena Early Diagnosis and Progress Characterisation of Neurodegenerative Diseases: A Systems Approach for Novel Bio Markers Fei He
Sivasharmini Ganeshamoorthy Computational Biology and Gene Network Inference: Study the Effects of Diurnal Asymmetric Warming on Plant Defence and Growth Fei He
Evgeny Gorbunov Nonlinear Interactions in Plasma Turbulence at Kinetic Level Bogdan Teaca
Stephan Goerttler Network Inference and Machine Learning: Understanding Brain Connectivity and Neurological Disorders Fei He
Sara Sardari Activity Recognition using Digital Frame Streams for Monitoring Rehab Period

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