Research in Statistical Science is based on a blend of project-based research groups, multidisciplinary collaborations and individual research programmes.
The department's methodological research is organised into six areas:
- Biostatistics. This theme has a research programme that encompasses both applied health research and the development and evaluation of statistical methods.
- Computational Statistics. This theme is concerned with advancing the theory, methodology, algorithmic development and application of simulation based approaches, such as Markov Chain Monte Carlo, to statistical inference.
- Economics, Finance and Business. This theme is concerned with the application of statistical, econometric and machine learning methods to problems arising in economics, finance and business.
- General Theory and Methodology. The research carried out under this theme covers foundational and theoretical aspects of probability and inferential statistics, and generic statistical methodology.
- Multivariate and High Dimensional Data. This theme has a research programme that encompasses both the theoretical and methodological problems encountered when analysing multivariate and high dimensional data.
- Stochastic Modelling of Complex Systems. This theme covers the development of generic stochastic models and the investigation of their properties, as well as modelling and inference for applications in a range of physical and biological sciences.
Much of this work is interdisciplinary and involves collaborations within and outside UCL.
- Probability at UCL. Research in probability theory begins with fundamental properties of universal stochastic models, and spans applications in life sciences, mathematical physics, finance, insurance and ergodic theory.
- Methodology for Weather and Climate. This research group focuses on methodology for climate and weather modelling including uncertainty quantification, computer models, data assimilation and machine learning for climate.
- Statistics for Health Economics Evaluation. The activity of this group revolves around the development and application of Bayesian statistical methodology for health economic evaluation, e.g. cost-effectiveness or cost-utility analysis.