Our work uses mathematical, computational and experimental methods to understand human health and disease

 
 
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Machine learning & data analytics

Modern experiments produce large amounts of data. Our work aims to develop new techniques for analysing complex datasets, particularly time-series, single cell expression and heterogeneous clinical datasets. We combine statistical analysis with mechanistic modelling to better understand the causes of observed patterns.


 Network of collaborations at the University of Southampton

Structure & dynamics of networks

Complex systems can often be represented as networks. Our work explores the structural properties of these networks and the ways in which network architecture relates to system behaviour. We have particular a interest in information flow in complex systems and stochastic processes on networks.


 Neural progenitors derived from mouse ES cells

Stem cell biology

We use experimental methods and mathematical models to investigate molecular regulation of stem cell fate. Our models include both deterministic and stochastic mechanisms, and we have a particular interest in cell-cell variability and its role in collective decision-making.