Research Areas

High Dimensional Data Analysis for Health

• Developing the PHESANT software package for conducting phenome scans (pheWAS, MR-pheWAS) in UK Biobank. PHESANT can also be used to generate a cleaned version of UK Biobank phenotype data for large scale analyses.
• MR-pheWAS studies, searching for the causal effect of an exposure of interest on many outcomes.
• Derive clean datasets for large-scale analyses
• Time-series data analysis of digital health data
• The dynamic relationship between genotype and phenotype, and how it varies over time and space. 
• Genome-wide analysis of high-dimensional multi-omic data using machine learning approaches specifically designed for “large-p small-n" data, these include methods for stable feature selection and addressing multi-collinearity.

Novel Digital Data Collection & Analysis for Health

Understand how we can use novel digital footprint data to study human behaviour and real-life outcomes, such as health.  

• Linking digital footprints data into cohorts (developing ground truth in smart data research) 
• Transaction data, specifically loyalty and banking cards, and working on realising the value of using these data to improve population health.
• Using machine learning to analyse and make predictions about fertility using intra-vaginal measurements of basal body temperature.
• Ethical and legal issues around linking data  
• Research in domain of reproductive health, diet, pain management with shopping data  
• Using shopping data for decision-making research  
• Deriving novel phenotypes from digital health data, including accelerometers and continuous glucose monitoring data 
• Molecular measurements in peripheral tissues (e.g. blood and saliva) for non-invasive risk stratification and early detection of cancer.
• Molecular epidemiology, Statistical prediction and development of genomic biomarkers.

Data Visualisation (keeping humans in the loop) 

• Developing new ways to present complex data sets for research and for public outreach.
• Developing mapping techniques to display novel COVID-19 datasets.
• Innovative public engagement shopping data visualisation tooling.

Trusted Research Environments for Linked Data

• Exploring optimal ways of working with extremely large linked datasets, such as electronic health records, available within secure, privacy-protecting Trusted Research Environments (TREs; also known as Secure Data Environments (SDEs)).