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 researchers
| Name | Contact |
|---|---|
| Dr Alireza Daneshkhah | alireza.daneshkhah@coventry.ac.uk |
| Dr Fei He | fei.he@coventry.ac.uk |
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 | klepld@uni.coventry.ac.uk |
| Ibnu Febry Kurniawan | Internet Of Things-Enabled Contextual Traffic Information Dissemination: Network Design And Performance Optimisation | Fei He | kurniawani@uni.coventry.ac.uk |
| James Donelly | A Novel Predictive Model for Extreme Climatic Events: Application of Catastrophe Modelling in Evaluating Environmental Hazard and Vulnerability | Ali Reza Daneshkhah | donnel39@uni.coventry.ac.uk |
| Majdi Fanous | Enhancing Mangrove Forest Resilience Against Coastal Degradation and Climate Change Impacts Using Advanced Bayesian Machine Learning Methods | Ali Reza Daneshkhah | fanousm2@uni.coventry.ac.uk |
| Shenal Gunawardena | Early Diagnosis and Progress Characterisation of Neurodegenerative Diseases: A Systems Approach for Novel Bio Markers | Fei He | gunawardes@uni.coventry.ac.uk |
| Sivasharmini Ganeshamoorthy | Computational Biology and Gene Network Inference: Study the Effects of Diurnal Asymmetric Warming on Plant Defence and Growth | Fei He | ganeshamos@uni.coventry.ac.uk |
| Evgeny Gorbunov | Nonlinear Interactions in Plasma Turbulence at Kinetic Level | Bogdan Teaca | gorbunove@uni.coventry.ac.uk |
| Stephan Goerttler | Network Inference and Machine Learning: Understanding Brain Connectivity and Neurological Disorders | Fei He | goerttlsardaris@uni.coventry.ac.ukers@uni.coventry.ac.uk |
| Sara Sardari | Activity Recognition using Digital Frame Streams for Monitoring Rehab Period | sardaris@uni.coventry.ac.uk |
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