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Dr Jake Carson

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Dr Jake Carson

Research Fellow
Office: MB 5.25


I am a research fellow in the Health Protection Research Unit in Genomics and Enabling DataLink opens in a new window. My research focuses on developing statistical methodology for integrating genomic data into epidemiological analyses.

Previously I was a research fellow in the Statistics Department at the University of Warwick on the project Novel Bayesian Methods for Comparing and Evaluating Infectious Disease Models in the Light of Partially Observed Data. In this project I developed approaches for estimating the marginal likelihood of individual-level infectious disease models that are scalable to the size of the population.

Before this, I worked on the project In Situ Nanoparticle Assemblies for Healthcare Diagnostics and Therapy, which sought to develop Raman spectroscopy as a diagnostic tool for heart disease. I contributed to the development of Bayesian approaches for constructing probabilistic representations of Raman spectra, and developed a Bayesian regression approach for multiplex quantification of Raman spectra that utilizes these probabilistic representations.

I completed my PhD at the University of Nottingham. My PhD thesis Uncertainty Quantification in Palaeoclimate Reconstruction looks at parameter estimation, state estimation, and model selection for phenomenological models of the climate using data from sediment cores.

My publications and preprints can be found below:

J. Carson, M. Keeling, D. Wyllie, P. Ribeca, and X. Didelot, Inference of infectious disease transmission using multiple genomes per host, bioRxiv 2023.07.28.550949Link opens in a new window.

J. Carson, A. Ledda, L. Ferretti, M. Keeling, and X. Didelot, The bounded coalescent model: conditioning a genealogy on a minimum root date, Journal of Theoretical Biology, 548:111186, 2022, doi:10.1016/j.jtbi.2022.111186Link opens in a new window.

J. Carson, T. J. McKinley, P. Neal, and S. E. F. Spencer, Efficient Bayesian model comparison for coupled hidden Markov models with application to infectious diseases, arXiv:2105.11807Link opens in a new window.

H. Blade et al., Conformations in solution and in solid-state polymorphs: Correlating experimental and calculated nuclear magnetic resonance chemical shifts for tolfenamic acid, Journal of Physical Chemistry A 124:8959-8977, 2020. doi:10.1021/acs.jpca.0c07000Link opens in a new window.

J. Carson, M. Crucifix, S. Preston and R. D. Wilkinson, Quantifying Age and Model Uncertainties in Paleoclimate Data and Dynamical Climate Models with a Joint Inferential Analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 475:20180854, 2019. doi:10.1098/rspa.2018.0854Link opens in a new window.

J. Noonan et al., In vivo multiplex molecular imaging of vascular inflammation using surface-enhanced Raman spectroscopy, Theranostics, 8:6195-6209, 2018. doi:10.7150/thno.28665Link opens in a new window.

M. T. Moores et al., Bayesian modeling and quantification of Raman spectroscopy, arXiv:1604.07299Link opens in a new window.

J. Carson, M. Crucifix, S. Preston and R. D. Wilkinson, Bayesian model selection for the glacial-interglacial cycle, Journal of the Royal Statistical Society: Series C (Applied Statistics), 67:25-54, 2018. doi:10.1111/rssc.12222Link opens in a new window.

J. Carson, Uncertainty Quantification in Palaeoclimate Reconstruction, PhD Thesis, University of Nottingham, July 2015. ePrintsLink opens in a new window.