Dr James Salter
Postdoctoral Research Associate
Currently funded by the Alan Turing Institute, working on UQ Frontiers project with Peter Challenor and Daniel Williamson. Also spend part of the time working on a WHO/WMO project with Gavin Shaddick et al., modelling global air pollution (contributions to PM2.5 from dust and other sources).
Research interests: emulating and history matching (calibrating) expensive computer models with high-dimensional output.
CAMPUS, March - April 2019
Combining Autonomous observations and Models for Predicting and Understanding Shelf Seas, with Prof. Peter Challenor.
World Health Organization, November 2017 - February 2019
Modelling global ambient air pollution (PM2.5), and the effect this has on various causes of death, with Prof. Gavin Shaddick and others.
Innovate UK project with Applegate, January 2018 - July 2018
Machine learning-based project with a local company, developing an algorithm to automate part of their business, with Prof. Richard Everson, and Dr Fabrizio Costa.
Past Earth Network Post-doc, April 2017 - October 2017
PEN funded project, entitled "Searching for the deglaciation: spatio-temporal boundary condition uncertainty and its implications for understanding abrupt climate change", with Daniel Williamson (Exeter) and Lauren Gregoire (Leeds).
PhD, University of Exeter, September 2013 - March 2017
Title: Uncertainty quantification for spatial field data using expensive computer models: refocussed Bayesian calibration with optimal projection
Supervised by Daniel Williamson
Salter, J. M., & Williamson, D. B. (2019). Efficient calbration for high-dimensional computer model output using basis methods. In submission. Available at https://arxiv.org/abs/1906.05758
Salter, J. M., Williamson, D. B., Gregoire, L. J., & Edwards T.L. (2018). Quantifying spatio-temporal boundary condition uncertainty for the deglaciation. In submission. Available at https://arxiv.org/abs/1808.09322
Salter, J. M., Williamson, D. B., Scinocca, J., & Kharin, V. (2018). Uncertainty quantifi[c]cation for spatio-temporal computer models with calibration-optimal bases. Journal of the American Statistical Association, 1-24.
Salter, J. M., & Williamson, D. (2016). A comparison of statistical emulation methodologies for multi-wave calibration of environmental models. Environmetrics, 27(8), 507-523.
Williamson, D., Blaker, A. T., Hampton, C., & Salter, J. (2015). Identifying and removing structural biases in climate models with history matching. Climate Dynamics, 45(5-6), 1299-1324.