Biography & CV

Very brief biography

I'm Jonty Rougier. I am a 'full spectrum' applied statistician, although the largest part of my academic work has been in Earth and Environmental Sciences. I've worked with several UK Government departments and agencies, and also with non-profit organizations and SMEs.

Brief biography

Jonathan Rougier is an applied statistician who works across the full range of theoretical, applied, and computational statistics.  His original training was in Economics (up to PhD level). After graduating from the University of Durham with a 1st class BSc in Economics (1988), he worked for two years as an economist for a large fund-management company.  He then returned to Durham as a Lecturer in Economics (1990), completing a PhD in 1996.

In 1997 he transferred to Mathematics at Durham to work as a Postdoctoral Researcher with Prof Michael Goldstein, in the area known as ‘Design and Analysis of Computer Experiments’.  In 2007 he took up a Lectureship in the School of Mathematics at the University of Bristol, being promoted to Reader in 2012 and to Professor in 2016 (Professor of Statistical Science). Since coming to Bristol he has collaborated extensively across the Faculties of Science and Engineering, including in volcanology, glaciology and palaeoclimate reconstruction, flooding, and in the more general area of uncertainty and risk assessment.  He has worked with several UK Government departments and agencies, and also with non-profit organizations and SMEs.

Activities

I do science in areas of high uncertainty and/or high value. Here are my main research/activity areas.

  • Risk assessment for low-probability high-impact events. This is Small Complicated Data: it is crucial to recognize and treat selective missingness in our historical records of events, both natural and man-made.

  • Spatial statistics at global scale. This is Big Complicated Data, where the challenge is to find a good compromise between what we would like to compute and what we can compute, and then compute it.

  • Theory of statistical inference and prediction. I have found that—as a practising statistician—it is important to have a sound grasp of the theoretical and philosophical fundamentals of Statistics, and also of the history of our subject.

  • Statistical consulting. For fellow scientists, and a range of public, private, and third sector organizations. How to work with organisations to add value through data is a very interesting question.  I always aim for the 3 U’s: Useful, Usable, and Used.

Additional resources