Born in Winnipeg, John Braun studied Applied Mathematics at the University of Calgary where he earned his BSc and MSc degrees. In 1992 he completed his PhD in Statistics at the University of Western Ontario under the supervision of Reg Kulperger. His research involved the study of point processes and other stochastic processes and, in his thesis, he developed numerical techniques to study parameter identifiability questions for an interacting particle system that had been proposed as a model for tumour growth.  
 

John has held positions at a number of universities, starting with the University of Winnipeg, where he began a collaboration with a psychophysicist in developing point process models for human reaction time. He spent most of 1999 at the Australian National University, working with the late Peter Hall and several of his postdocs on a variety of smoothing and bootstrapping problems. In 2000 he accepted a position at Western University where he attained the rank of full professor and where for five years, he chaired the Statistics Graduate Program. While at Western University he worked with a number of collaborators both there and at the University of Toronto to develop an interacting particle system-based wildfire growth model. With some enterprising PhD students, he also studied lightning-based wildfire ignitions and storm cell evolution using point process techniques.
 

In 2014 he became head of the Department Computer Science, Mathematics, Physics and Statistics at UBC Okanagan. The following year John began serving the Canadian Statistical Sciences Institute (CANSSI) as Deputy Director and co-leading the Rocky Mountain Data Science Centre, a joint Health Sciences training initiative of the University of Calgary and UBC under the sponsorship of CANSSI.  
 

John’s research in statistics has often been motivated by scientific problems coming from psychology, biology, medicine, engineering and physics. His methodological research is concerned with smoothing and inference techniques as they apply to data visualization and process monitoring. John’s research on stochastic modelling and statistical inferencing for large-scale spatial-temporal data sets has led to numerous research projects for a diverse range of applications. John collaborated with the Institute for Catastrophic Loss Reduction (ICLR), the Canadian Forest Service, Ontario Ministry of Natural Resources & Forestry and Alberta Wildfire to study wildland fire regime modelling. He has been instrumental in providing statistical approaches to wildfire modelling and management. His work on Prometheus, the wildland fire growth and spread model, has produced variants that can provide probabilistic forecasts for a particular fire in real time.  
 

In addition to his research contributions, John has impacted the promotion and advancement of the R language for statistical computing. He authored and co-authored two monographs on using R, which are in their 2^nd and 3^rd editions. These materials are excellent resources for statistical education, training and research. In addition, John has submitted six R packages to CRAN which have been widely used by researchers, students, engineers, and practitioners.
 

The excellence of John’s collaborative work was recognized by his receipt of The Canadian Journal of Statistics Award in 2006 and also by his students receiving a presentation award (2015) and a poster award (2009) at the annual meetings of the Statistical Society of Canada.