I am a research fellow on the MRC funded project “Novel Bayesian Methods for Comparing and Evaluating Infectious Disease Models in the Light of Partially Observed Data” (ref MR/P026400/1) with Dr Simon Spencer. Currently my research is focussed on developing Bayesian methods for comparing infectious disease models that are scalable to the size of the population.
Between 2014-2017 I worked on the EPSRC funded project “In Situ Nanoparticle Assemblies for Healthcare Diagnostics and Therapy” (ref EP/L014165/1) at the University of Warwick (2014-2016) and Imperial College London (2017) with Prof Mark Girolami. My main contributions were in the development of Bayesian methodology for biomarker quantification from Raman spectra. Frequently, Raman spectra undergo preprocessing to remove the non-informative hidden baseline. This type of preprocessing can bias subsequent analyses due to the uncertainty in the baseline estimate being ignored. I developed approaches to quantifying Raman spectra without first needing to remove the baselines, overcoming this problem.
My doctoral thesis “Uncertainty Quantification in Palaeoclimate Reconstruction” was under the supervision of Prof Richard Wilkinson and Dr Simon Preston at the University of Nottingham. As with Raman spectra, paleocliamate data often undergo preprocessing that influences later analyses, i.e. dating a sediment core to convert the depths of core slices into ages prior to calibrating a climate model. My research focussed on developing efficient approaches for performing inferential analyses that incorporate the uncertainties from the preprocessing stages.
As my involvement on these projects demonstrate, my main research interests are computational Bayesian statistics and its application to real-world problems with large amounts of missing information, and developing efficient approaches for integrating uncertainty from data preprocessing stages into downstream analyses in order to generate robust conclusions.
J. Carson, M. Crucifix, S. Preston and R. D. Wilkinson, Quantifying Age and Model Uncertainties in Paleoclimate Data and Climate Models with a Joint Inferential Analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 475, 2019. Preprint - Postprint - Published
J. Noonan et al., In Vivo Multiplex Imaging of Vascular Inflamation Using Surface-Enhanced Raman Spectrscopy, Theranostics, 8:6195-6209, 2018. Published
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. Preprint - Postprint - Published
M. Moores, K. Gracie, J. Carson, K. Faulds, D. Graham and M. Girolami, Bayesian modelling and quantification of Raman spectroscopy. arXiv:1604.07299.
J. Carson, M. Pollock and M. Girolami, Unbiased local solutions of PDE models via the Feynman-Kac identities. arXiv:1603:04196.
Mathematical Sciences Building
Department of Statistics
University of Warwick
CV4 7AL, UK
jake dot carson at warwick dot ac dot uk