Statistics and Data Science | Statistical Science

Statistical modelling techniques help to correct forecast biases in numerical weather and climate models.

We develop statistical tools to evaluate the skill and reliability of weather and climate forecasts

We develop statistical methodology for calibrating computer simulation models, using statistical emulators that can be evaluated quickly in place of the expensive computer model.

We develop flexible statistical tools to approximate the output of complex systems, to efficiently explore the possible behaviors of the model and propagate uncertainties from model inputs to the final predictions.

Combining large amounts of data from different sources (satellites, station measurements, indirect predictors) produces global estimates of air pollution at high spatial and temporal resolutions.

We have a world-renowned track record in the statistical modelling of risk due hydro-meteorological hazards.

Statistical disease risk models, incorporating environmental factors and climate information, assist in public health decision making.

Research

Our group works on a wide range of exciting research topics, including environmental hazards, air quality modeling, calibration of computer models, forecast verification and spatial epidemiology.

Opportunities

We welcome enquiries from prospective PhD students. You can also find more under the personal webpages of the group members.

Teaching

We teach Statistics courses at all levels, covering a range of applied and theoretical topics, such as:

Probability, Statistics and Data
Statistical Modelling and Inference
Bayesian statistics, Philosophy and Practice
Statistical Modelling in Space and Time