Title: The fine art of putting a bunch of straight lines through things Abstract:

Ambient air pollution is a global public health problem, but if we want to know how big a problem it is, we need to do some statistical modelling. The problem is that we do not have good measurements of air pollution everywhere that people live and so we cannot link exposure to adverse health outcomes. What we do have is a monitoring network, satellites, and a great deal of optimism. Now all we need is to turn that into a coherent estimate of air pollution. In this talk I will talk through the challenge of calibrating plentiful but inaccurate satellite measurements to rarer high-quality data. In the course of doing this I will touch on the principles of Bayesian modelling, techniques for approximating Gaussian random fields, and model assessment.

Daniel Simpson got his PhD from the Queensland University of Technology in 2009. Since 2017 he has been an Assistant Professor in the Department of Statistics at the University of Toronto and he is currently the Canadian Research Chair in Spatiotemporal Modelling. He was previously a Reader (Professor) at the University of Bath (UK) and has worked at the University of Warwick, the Norwegian University of Science and Technology (NTNU), the University of Helsinki, and Umeå University. He has been told that he is abnormally fond of musical theatre, but his real hobby seems to be getting work visas.