Professor Christl Donnelly is a Professor of Statistical Epidemiology at Imperial College London, a Professor of Applied Statistics at the University of Oxford and a Fellow of St Peters College, Oxford. She is internationally renowned in statistical approaches to the study of infectious disease, particularly for her work on the bovine TB, H1N1 influenza, SARS and Ebola viruses. She is a senior member of the Imperial College COVID-19 Response Team, which has informed government response to the pandemic in the UK and beyond.  Her research interfaces with ecology and conservation, including current projects studying badger culling and mosquito control techniques, and their connections to the spread of infectious disease. Christl’s work spans theory and practice, from the development of new statistical tools for the study of disease spread to providing policy advice to government. Her work has been recognised with prestigious honours including fellowship of the Royal Society and the Academy of Medical Sciences, and appointment as a Commander of the Order of the British Empire.

Professor David Borchers has made numerous high-impact contributions to the theory and practice of wildlife population assessment. He is particularly well-known as a pioneer of spatial capture-recapture methods, and for his work on double-observer surveys from ships and aircraft. His first degree was in psychology at the University of Cape Town, after which he saw the light and specialized in statistics, further being converted to statistical ecology by his first job with the International Whaling Commission in Cambridge. He joined the University of St Andrews in 1994, where he led the prolific Research Unit for Wildlife Population Assessment for 12 years. He has over 100 publications, including 4 books, and continues to travel to fascinating places all over the world to assist with wildlife surveys. On any given day he could be found chasing snow leopards in Kyrgyzstan, eavesdropping on moss frogs in South Africa, or just hanging out at home with Cambodian gibbon experts.

Dianne Cook is Professor of Business Analytics at Monash University in Melbourne, Australia. She is a world leader in data visualisation, especially the visualisation of high-dimensional data using tours with low-dimensional projections, and projection pursuit. Dianne is a Fellow of the American Statistical Association, was recently the editor of the Journal of Computational and Graphical Statistics, and has been elected as an Ordinary Member of the R Foundation. Several of her students have won the prestigious American Statistical Association John Chambers Software Award, including Hadley Wickham, Yihui Xie, Carson Sievert, and most recently, Monash student Earo Wang. Dianne is currently focussing on bridging the gap between exploratory graphics and statistical inference.

Kiona Ogle is a Professor in the School of Informatics, Computing, and Cyber Systems at Northern Arizona University.  Her research uses advanced statistical tools in an integrative framework to develop a mechanistic understanding of carbon and water cycling in arid and semi-arid systems, soil carbon dynamics, and the role of antecedent conditions in governing ecological responses (“ecological memory”), especially in the context of plant responses to environmental perturbations. Kiona’s training was similarly interdisciplinary, with postgraduate degrees in biology (PhD) and statistics (MS) from Duke University.  

Mark Bravington is a statistician at the CSIRO’s Marine Laboratories in Hobart, Australia. His research focuses on statistical methods development for natural resource management, especially in fisheries and in marine mammal conservation: population dynamics, abundance estimation, distribution, etc. Over the past decade, he has increasingly concentrated on developing close-kin mark-recapture as a powerful and practical tool: genetic aspects, statistical model-building, and study design. His research is aimed at some of wildlife management's hardest problems, and relies on a deep (but self-proclaimed patchy) understanding of statistics, biology, genetics, and management processes.