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Research Aims

This programme aims to be leading in terms of the level of interaction between research into fundamentally new data science methods and applications of data science to cutting-edge biomedical and health science. It will not only deliver new insights across a range of health science applications, but also a new generation of widely-applicable Bayesian data science methods and new communities of researchers working at the interface of probabilistic modelling and health and biomedical science.

Methodological Themes

  • Scalable Monte Carlo Methods

    Monte Carlo methods have enabled Bayesian statistics to be applied routinely. This theme will develop novel version of algorithms such as MCMC that have excellent computational properties and that are efficient in big-data scenarios and can take advantage of the power of parallel computing.

  • Fusing Information from Disparate Sources

    We will develop principled and practicable Bayesian approaches that enable information to be combined across different data sources types, such as health records, genotype information and, perhaps, image data from a fMRI scan.

  • Robust Bayesian Methods

    Bayesian methods necessarily make modelling assumptions which will not hold exactly. We will develop novel Bayesian-type approaches that acknowledge, account for, and, hence, are robust to such model error.

Motivating Challenges

  • Monitoring of Epidemics

    Prompt public health responses to outbreaks of epidemics relies on real-time monitoring and prediction of their spread.

  • Genomics and Phenotyping

    There is currently a step change in the number of very large cohort studies that are collecting and analysing detailed genetic information as well as rich phenotypic data.

  • Personalized Medicine

    Being able to customize medical care to each individual patient has huge potential for improving patient care through earlier diagnoses and timely and more appropriate interventions and medication.