Rowland Seymour
Associate Professor of Statistics
University of Birmingham
Rowland Seymour
Associate Professor of Statistics
University of Birmingham
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Associate Professor of Statistics
University of Birmingham
I am an Associate Professor of Statistics and UKRI Future Leaders Fellow at the University of Birmingham. I am a statistician developing Bayesian methods for applications in social and health sciences. My current interests include developing statistical prevalence estimation methods for different crime types and applications of Bayesian nonparametric methods.
I am an Associate Professor of Statistics and UKRI Future Leaders Fellow at the University of Birmingham. I am a statistician developing Bayesian methods for applications in social and health sciences. My current interests include developing statistical prevalence estimation methods for different crime types and applications of Bayesian nonparametric methods.
News
October 2025: Prof Ian Jones has 'hopped' to my group through an EPSRC Discipline Hopping grant to work on comparative judgement.
October 2025: New preprint on Statistical Crime Linkage: Evaluating approaches within the Covenant for Using AI in Policing.
June 2025: I have joined the Home Office Science Advisory Council.
May 2025: New paper in the Journal of the Royal Statistical Society Series C on a partially pooled network scale-up method model: detailed estimation of child sexual exploitation material trafficking prevalence in Philippine municipalities.
March 2025: New paper in the Annals of Applied Statistics on Comparative judgement modelling to map forced marriage at local levels.
December 2024: New paper in the Journal of Applied Statistics on Scalable Bayesian inference for Bradley–Terry models with ties: an application to honour based abuse.
November 2024: New R package speedyBBT, which provides a suite of functions that allow a full, fast, and efficient Bayesian treatment of the Bradley–Terry model.
October 2024: New paper in the Journal of Public Health on Cognitive impairment and exploitation: connecting fragments of a bigger picture through data.
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