I am a Principal Lecturer in Statistics at the University of Brighton, where I serve as Course Leader for the BSc in Data Science and the MSc in Artificial Intelligence and Machine Learning. My research specialises in generalised estimating equations (GEE), statistical methods for longitudinal and correlated data, and high-dimensional analysis. My work spans methodological research and open-source software development, with four R packages published on CRAN and one peer-reviewed package on Bioconductor, cited in published statistics textbooks and with over 313,000 combined downloads.

I trained in Statistics at the University of Florida (PhD, supervised by Alan Agresti) before joining the University of Cambridge and EMBL-EBI as a joint postdoctoral fellow, working on high-dimensional and genomic data with Simon Tavaré and John Marioni. I have applied these methods to clinical, observational and genomics datasets across oncology, musculoskeletal outcomes and cancer research, and I hold NIHR Good Clinical Practice (ICH E6(R3)) certification.

My research centres on statistical methods for correlated and longitudinal data, particularly generalised estimating equations (GEE) for multinomial, binary and count responses, including bias-reduction and penalisation techniques that address separation and small-sample bias. Two manuscripts on bias-reduced and Jeffreys-type penalised GEE are currently under review at Biometrics and Statistics in Medicine. I also work on high-dimensional statistics, including covariance matrix estimation and hypothesis testing for multivariate and genomic data.

My research is motivated by cross-disciplinary collaborations with clinicians, biologists and bioinformaticians, and has been applied to health studies, clinical datasets and omics data, alongside applications in the social sciences. I disseminate the methods I develop as open-source R packages on CRAN and Bioconductor, with an emphasis on numerical accuracy and rigorous software validation, the same standards expected of statistical software in regulated research environments.

I teach a wide range of undergraduate and postgraduate statistical modules at the University of Brighton, to both specialist and non-specialist audiences, including medical statistics, statistical modelling, survival analysis, epidemiological methods and clinical trial design principles. My approach prioritises genuine understanding of statistical concepts over memorisation of technical procedures, encouraging independent, critical thinking rather than rote application of methods.

A recurring theme in my teaching is translating statistical ideas for audiences without a mathematics background, a skill that carries directly into cross-functional collaboration outside academia. I incorporate contemporary statistical software, including R and SAS, into my teaching material so that students build practical, workplace-relevant technical skills alongside theoretical understanding.

I am interested in supervising postgraduate research students in categorical data analysis, multivariate analysis, high-dimensional statistics and machine learning methods, with a particular interest in methods for correlated and longitudinal data. Projects typically involve developing open-source software (often destined for CRAN or Bioconductor) to implement, validate and disseminate research findings, giving students direct experience in reproducible, production-quality statistical software development alongside the underlying methodology

I actively translate statistical methodology into practical applications beyond academic publication. As Co-Investigator on the DRIVA project (Digital Research and Innovation Value Accelerator, funded by the European Regional Development Fund, £445,685), I gathered stakeholder requirements from small and medium-sized enterprises and developed gatwickAPI, an R package enabling businesses to access and analyse Gatwick Airport's real-time flight data, alongside delivering training sessions on user experience design for participating businesses.

I have led a client-facing statistics consultancy team providing methodological support to the University of Brighton's Doctoral College and to Brighton and Sussex Medical School, working directly with researchers and clinicians to design and analyse studies, generating over £20,000 in revenue. I have also served as External Examiner for the MSc in Applied Statistics at Birkbeck, University of London, and as External Advisor on curriculum development for biomedical data sciences at Queen Mary University of London.