Staff profile

• Statistics
• Applied Statistics
• Uncertainty Analysis
• Statistical Computation
• Variable Selection

Chapter in book

• Bayes Linear Emulation of Simulated Crop Yield
  
  Hasan, M. M., & Cumming, J. A. (2021). Bayes Linear Emulation of Simulated Crop Yield. In Y. P. Chaubey, S. Lahmiri, F. Nebebe, & A. Sen (Eds.), Applied Statistics and Data Science:Proceedings of Statistics 2021 Canada, Selected Contributions (pp. 145-151). Springer Verlag. https://doi.org/10.1007/978-3-030-86133-9_7
• Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches
  
  Errington, A., Einbeck, J., & Cumming, J. (2021). Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches. In M. Vasile & D. Quagliarella (Eds.), Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications (pp. 393-405). Springer Verlag. https://doi.org/10.1007/978-3-030-80542-5_24
• Bayes linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments
  
  Cumming, J., & Goldstein, M. (2010). Bayes linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments. In A. O’Hagan & M. West (Eds.), The Oxford handbook of applied Bayesian analysis. (pp. 241-270). Oxford University Press.

Conference Paper

• Field Applications of Constrained Multiwell Deconvolution
  
  Jaffrezic, V., Razminia, K., Cumming, J., & Gringarten, A. (in press). Field Applications of Constrained Multiwell Deconvolution. Presented at SPE Europec featured at 81st EAGE Conference and Exhibition, London, UK.
• Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach
  
  Cumming, J., Botsas, T., Jermyn, I., & Gringarten, A. (2020). Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach. In SPE Virtual Europec 2020 ; proceedings. (p. SPE-200617-MS). Society of Petroleum Engineers (SPE). https://doi.org/10.2118/200617-ms
• Using Deconvolution to Estimate Unknown Well Production from Scarce Wellhead Pressure Data
  
  Aluko, L., Cumming, J., & Gringarten, A. (2020, October 19). Using Deconvolution to Estimate Unknown Well Production from Scarce Wellhead Pressure Data. Presented at SPE Annual Technical Conference and Exhibition, Virtual.
• A Bayesian non-linear hierarchical framework for crop models based on big data outputs
  
  Hasan, M. M., & Cumming, J. (2020). A Bayesian non-linear hierarchical framework for crop models based on big data outputs [Conference paper]. Presented at 13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), King’s College London, England.
• Constrained Least-Squares Multiwell Deconvolution
  
  Cumming, J., Jaffrezic, V., Whittle, T., & Gringarten, A. (2019). Constrained Least-Squares Multiwell Deconvolution. In Proceedings of the SPE Western Regional Meeting 2019. Society of Petroleum Engineers (SPE). https://doi.org/10.2118/195271-ms
• Multiwell Deconvolution for Shale Gas
  
  Tung, Y., Virues, C., Cumming, J., & Gringarten, A. (2016, May). Multiwell Deconvolution for Shale Gas. Presented at SPE Europec featured at 78th EAGE Conference and Exhibition, Vienna, Austria. https://doi.org/10.2118/180158-ms
• Application of Multiple Well Deconvolution Method in a North Sea Field
  
  Thornton, E., Mazloom, J., Gringarten, A., & Cumming, J. (2015). Application of Multiple Well Deconvolution Method in a North Sea Field. Presented at EUROPEC 2015, Madrid, Spain. https://doi.org/10.2118/174353-ms
• Multiple Well Deconvolution
  
  Cumming, J., Wooff, D., Whittle, T., & Gringarten, A. (2013). Multiple Well Deconvolution. Presented at 2013 SPE Annual Technical Conference & Exhibition, New Orleans, USA. https://doi.org/10.2118/166458-ms
• Assessing the Non-Uniqueness of the Well Test Interpretation Model Using Deconvolution
  
  Cumming, J., Wooff, D., Whittle, T., Crossman, R., & Gringarten, A. (2013). Assessing the Non-Uniqueness of the Well Test Interpretation Model Using Deconvolution. Presented at 75th EAGE Annual Conference & Exhibition, 10–13 June 2013, London, United Kingdom. https://doi.org/10.2118/164870-ms

Doctoral Thesis

• Clinical Decision Support
  
  Cumming, J. (2006). Clinical Decision Support [Thesis]. Department of Mathematical Sciences, Durham University. https://www.maths.dur.ac.uk/~dma3jac/thesis.pdf

Journal Article

• Systematic structural discrepancy assessment for computer models
  
  Goldstein, M., Vernon, I., & Cumming, J. A. (2025). Systematic structural discrepancy assessment for computer models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 383(2293), Article 20240214. https://doi.org/10.1098/rsta.2024.0214
• A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis
  
  Botsas, T., Cumming, J., & Jermyn, I. (2022). A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(4), 951-968. https://doi.org/10.1111/rssc.12562
• The effect of data aggregation on dispersion estimates in count data models
  
  Errington, A., Einbeck, J., Cumming, J., Rössler, U., & Endesfelder, D. (2022). The effect of data aggregation on dispersion estimates in count data models. International Journal of Biostatistics, 18(1), 183-202. https://doi.org/10.1515/ijb-2020-0079
• Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control
  
  Goldie, S. J., Bush, S., Cumming, J. A., & Coleman, K. S. (2020). Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control. ACS Applied Nano Materials., 3(11), 11229-11239. https://doi.org/10.1021/acsanm.0c02361
• 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
• Multiwell Deconvolution
  
  Cumming, J., Wooff, D., Whittle, T., & Gringarten, A. (2014). Multiwell Deconvolution. SPE Reservoir Evaluation and Engineering., 17(04), 457-465. https://doi.org/10.2118/166458-pa
• Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations
  
  Cumming, J., & Goldstein, M. (2009). Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations. Technometrics, 51(4), 377-388. https://doi.org/10.1198/tech.2009.08015
• Dimension reduction via principal variables
  
  Cumming, J., & Wooff, D. (2007). Dimension reduction via principal variables. Computational Statistics & Data Analysis, 52(1), 550-565. https://doi.org/10.1016/j.csda.2007.02.012

Report

• Understanding the accuracy of pre-symptomatic diagnosis of sepsis
  
  Cumming, J., Riseth, A., & Williams, J. (2016). Understanding the accuracy of pre-symptomatic diagnosis of sepsis.