Dr Julia Brettschneider
  • High-dimensional quality assessment and control for free-form objects in 3D printing/additive layer manufacturing
  • Preprocessing of biological time series invoking a priori known information
Professor Bärbel Finkenstädt Rand
  • Non-stationary spectral analysis of high frequency biomarker data for chronotherapy
  • Modelling and Inference for gene regulatory networks
  • Spatio-temporal modelling of gene expression
Professor David Firth
  • Fast, accurate and reliable calibration of statistical models with massive     datasets (uses ideas from differential geometry, numerical analysis and sampling theory)
  • Approximate conditional inference with large-scale data streams (a blend of statistical theory and computational statistics)
  • Longitudinal bibliometrics: Statistical modelling of the "careers" of published research articles, based on historical citation data (statistical modelling coupled with large-scale data management and analysis)
Professor Mark Girolami
  • Variance Reduction for Russian Roulette Sampling for large scale streaming data models
  • Computational scaling of Russian Roulette Sampling to massive scale statistical models (e.g. in geo-physical statistical models)
  • Statistical characterisation of Deep Learning with application to acoustic based population-density estimation of bat populations across Europe
  • Big Data meets Big Models - what are the underlying principles of and how does one assimilate high volume data streams into humongous PDE models describing e.g. global monitoring of ozone levels and subsurface fluid flows
  • Bayesian methods for ultra-high dimensional data
Professor Jotun Hein
  • Models of Evolution
  • Origins of Life
  • Population Genetics
Professor Jane Hutton
  • Effective models for seizure rates to assess genetic predisposition to response or resistance to pharmacological treatments
  • Informative unit and item non-response in longitudinal studies
Dr Adam Johansen
  • Sequential Monte Carlo for Large Models: Divide and Conquer Approaches
  • Computational Strategies for Spatial Mixture Modelling with Application to Neuroimaging Data
 Professor Wilfrid Kendall
  • X-ray transforms and statistical geometry for additive layer manufacturing
Professor Chenlei Leng
  • Flexible models for large array data
  • A new canonical correlation analyses
Professor Jonathan Marchini
  • Neuroimaging genetics : Bayesian methods development for genetic association with high dimensional imaging phenotypes
  • Statistical genetics methodology for large-scale sequencing and genotyping projects
Professor Thomas Nichols
  • Neuromaging Genetics (Big Data Squared): Building sensitive models for genetic association with a high dimensional imaging phenotypes
  • Neuroimaging Meta-Analysis: Joint modelling of massive volume and point-valued imaging datasets
Professor Gesine Reinert
  • Probabilistic and statistical network comparisons
  • Analysis of protein-protein interaction networks
Professor Gareth Roberts
  • Adaptive MCMC for high-dimensional Monte Carlo
  • Pseudo-marginal MCMC using epsilon strong simulation
Professor Jim Smith
  • Dynamic graphical models on evolving domains
  • Tree models for system discovery in high dimensional domains
Dr Dario Spanò
  • Time-dependent cluster and feature models - probabilist and statistical aspects 
Dr Simon Spencer
  • Validation for network inference
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