Data science and statistics

We focus on the analysis of real-world dynamical systems that generate large data sets. We apply a broad range of mathematical techniques to identify system characteristics, and to make predictions using data and mathematical models.

Our research

Large datasets are now generated routinely in many areas. Extracting information from data requires both mathematical and computational analysis. Mathematics provides the theoretical underpinning and analytical tools necessary for extracting meaningful insights from data. We are particularly interested in:

Data assimilation methods, which use data to inform mathematical models.
Uncertainty quantification for weather and climate modelling.
Novel methods for the analysis of physiological time series which involves machine learning.
Design of experiments.

Data science and statistical design

Expertise in the design of experiments, the development of new data assimilation methods and novel methods for the analysis of physiological time series.

Storm clouds and lightening over the countryside

Stochastic modelling and uncertainty quantification

Developing prediction systems for weather and climate that consider unknown processes and errors in the mathematical models and measurements, to estimate uncertainties in forecasts.