Joseph Marsh is currently a Research Fellow working alongside Dr Rowland Seymour, where his research focuses on the intersection of Bayesian statistics and societal challenges. Their collaborative work involves developing robust Bayesian models and methods designed to tackle high-stakes problems across crime, health, and the social sciences.
Joseph's research is underpinned by a broad interest in Bayesian computation and the refinement of Markov chain Monte Carlo (MCMC) methodologies. He is particularly interested by the application of stochastic epidemic modelling and simulation-based inference to understand complex systems. Additionally, Joseph explores comparative judgement as a framework for quantifying subjective data in social contexts.
Dr Marsh earned his PhD from the University of Nottingham in 2023 under the supervision of Professors Phil O’Neill and Theodore Kypraios. His doctoral thesis, entitled "Models and methods to integrate epidemiological and whole genome sequence data for effectively analysing infectious disease outbreak data," focused on creating unified statistical frameworks to harness the power of genomic sequencing in public health. Joseph aims to develop scalable statistical tools that empower practitioners to make data-driven decisions in public policy and healthcare.