Invited Speaker: A/Prof Edward Cripps

July 26, 2020

Uncertainty quantification and communication for the earth sciences

Biography

Edward Cripps, deputy director of DARE centre (Data Analytics for Resources and Environments), is an Associate Professor in the Department of Mathematics and Statistics at the University of WA. His research interests are in Bayesian longitudinal analysis and spatio-temporal models, and the integration of statistical and physical models. His primary applications are in the statistical modelling of environmental, meteorological and oceanographic processes and their interaction with engineering decision making and asset management. Edward has extensive experience in industry collaboration and translating academic research output into commercially industrial applications. He is currently working in collaboration with Shell, Woodside, Lloyds and the Alan Turing Institute, the UK’s national data science institute.

Talk Overview:

Advances in technology and the availability of data-acquisition devices have increasingly centralised the role of the data analytics in the earth sciences, which in turn inform data driven decision making across science, industry and government. Still, despite the preponderance of data, empirical based decision making continues to be made under conditions of uncertainty: data is messy; statistical model selection/estimation is complex; underlying physics that discretised numeric methods attempt to resolve are mis-specified. This recognition implies that, when the consequences of decisions are substantial, robust uncertainty quantification ought to accompany the fusion of domain knowledge and empirical evidence. This talk is based on a series of recent papers, providing an overview on: recent applications/methods developed with earth scientists and industry partners for probabilistic models of meteorological, oceanographical and geophysical processes; experiences on conveying to non-statistical colleagues the meaning of uncertainty and its consequences for decision making; deployment of software for private (industry) and public use.