Director of SMCU, Assistant Professor, Statistics in the Department of Mathematical Sciences
Department of Mathematical Sciences
- Applied Statistics
- Uncertainty Analysis
- Statistical Computation
- Variable Selection
Chapter in book
- Cumming, J. A. & Goldstein, M. (2010). Bayes linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments. In The Oxford Handbook of Applied Bayesian Analysis. O'Hagan, A. & West, M. Oxford: Oxford University Press. 241-270.
- Tung, Y, Virues, C, Cumming, J A & Gringarten, A C (2016), Multiwell Deconvolution for Shale Gas, SPE Europec featured at 78th EAGE Conference and Exhibition. Vienna, Austria, SPE.
- Thornton, E J, Mazloom, J, Gringarten, A C & Cumming, J A (2015), Application of Multiple Well Deconvolution Method in a North Sea Field, EUROPEC 2015. Madrid, Spain, Society of Petroleum Engineers, Madrid.
- Cumming, J A, Wooff, D A, Whittle, T, Crossman, R J & Gringarten, A C (2013), Assessing the Non-Uniqueness of the Well Test Interpretation Model Using Deconvolution, 75th EAGE Annual Conference & Exhibition, 10–13 June 2013. London, United Kingdom, Society of Petroleum Engineers, London, 1-24.
- Cumming, J A, Wooff, D A, Whittle, T & Gringarten, A C (2013), Multiple Well Deconvolution, 2013 SPE Annual Technical Conference & Exhibition. New Orleans, USA, Society of Petroleum Engineers, New Orleans LA.
- Cumming, J A (2006). Clinical Decision Support. Department of Mathematical Sciences. Durham University. PhD.
- Vernon, I. R., Jackson, S. E. & Cumming, J. A. (2018). Known Boundary Emulation of Complex Computer Models. SIAM/ASA Journal on Uncertainty Quantification
- Cumming, J. A., Wooff, D. A., Whittle, T. & Gringarten, A. C. (2014). Multiwell Deconvolution. SPE Reservoir Evaluation and Engineering 17(04): 457-465.
- Cumming, J. A. & Goldstein, M. (2009). Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations. Technometrics 51(4): 377-388.
- Cumming, J. A. & Wooff, D. A. (2007). Dimension reduction via principal variables. Computational Statistics & Data Analysis 52(1): 550-565.
- Cumming, J. A., Riseth, A. & Williams, J. (2016). Understanding the accuracy of pre-symptomatic diagnosis of sepsis. Department of Mathematical Sciences. Durham, European Study Group in Industry 116 (ESGI 116).