Staff profile

• Bayes linear methods
• Bayesian emulation
• History matching
• Uncertainty quantification

Chapter in book

• Hotspots in Hindsight
  Julian, B. R., Foulger, G. R., Hatfield, O., Jackson, S. E., Simpson, E., Einbeck, J., & Moore, A. (2015). Hotspots in Hindsight. In The Interdisciplinary Earth: A Volume in Honor of Don L. Anderson (105-121). The Geological Society of America / AGU. https://doi.org/10.1130/2015.2514%2808%29

Journal Article

• Nitrogen isotope variability of macroalgae from a small fishing village, Staithes Harbour, Yorkshire, UK.
  Alldred, F. C., Gröcke, D. R., & Jackson, S. E. (2024). Nitrogen isotope variability of macroalgae from a small fishing village, Staithes Harbour, Yorkshire, UK. Marine Pollution Bulletin, 207, Article 116828. https://doi.org/10.1016/j.marpolbul.2024.116828
• Further investigation into the impact of manuring on stable carbon (δ13C) and nitrogen isotope (δ15N) values in pulses: a four-year experiment examining Celtic bean (Vicia faba)
  Treasure, E. R., Gröcke, D. R., Lester, J. J., Bishop, R. R., Jackson, S. E., & Church, M. J. (2024). Further investigation into the impact of manuring on stable carbon (δ13C) and nitrogen isotope (δ15N) values in pulses: a four-year experiment examining Celtic bean (Vicia faba). Archaeological and Anthropological Sciences, 16(8), Article 130. https://doi.org/10.1007/s12520-024-02045-x
• Nitrogen isotopes in herbaria document historical nitrogen sewage pollution in the Mersey Estuary, England
  Alldred, F. C., Gröcke, D. R., Jackson, S. E., & Reid, G. (2024). Nitrogen isotopes in herbaria document historical nitrogen sewage pollution in the Mersey Estuary, England. Environmental Science: Advances, 3(5), 676-685. https://doi.org/10.1039/D4VA00015C
• Extremism, segregation and oscillatory states emerge through collective opinion dynamics in a novel agent-based model
  Stokes, B., Jackson, S., Garnett, P., & Luo, J. (2024). Extremism, segregation and oscillatory states emerge through collective opinion dynamics in a novel agent-based model. The Journal of Mathematical Sociology, 48(1), 42-80. https://doi.org/10.1080/0022250X.2022.2124246
• Efficient Emulation of Computer Models Utilising Multiple Known Boundaries of Differing Dimension
  Jackson, S. E., & Vernon, I. (2023). Efficient Emulation of Computer Models Utilising Multiple Known Boundaries of Differing Dimension. Bayesian Analysis, 18(1), 165-191. https://doi.org/10.1214/22-ba1304
• Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching
  Jackson, S., Vernon, I., Liu, J., & Lindsey, K. (2020). Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching. Statistical Applications in Genetics and Molecular Biology, 19(2), Article 20180053. https://doi.org/10.1515/sagmb-2018-0053
• In situ statistical measurement of local morphology in carbon-epoxy composites using synchrotron X-ray computed tomography
  Rosini, S., Mavrogordato, M. N., Egorova, O., Matthews, E. S., Jackson, S. E., Mark Spearing, S., & Sinclair, I. (2019). In situ statistical measurement of local morphology in carbon-epoxy composites using synchrotron X-ray computed tomography. Composites Part A: Applied Science and Manufacturing, 125, https://doi.org/10.1016/j.compositesa.2019.105543
• Known Boundary Emulation of Complex Computer Models
  Vernon, I., Jackson, S., & Cumming, J. (2019). Known Boundary Emulation of Complex Computer Models. SIAM/ASA Journal on Uncertainty Quantification, 7(3), 838-876. https://doi.org/10.1137/18m1164457
• The correlation threshold as a strategy for gene filtering, with application to irritable bowel syndrome and breast cancer microarray data
  Jackson, S. E., Einbeck, J., Kasim, A., & Talloen, W. (2016). The correlation threshold as a strategy for gene filtering, with application to irritable bowel syndrome and breast cancer microarray data. Reinvention, 9(2),
• A summer with genes: Simple disease classification from microarray data
  Einbeck, J., Jackson, S. E., & Kasim, A. (2015). A summer with genes: Simple disease classification from microarray data. Mathematics today, 51(4), 186-188