- Chief Investigators
- Phil Pollett
Professor Phil Pollett
Professor Phil Pollett is recognised internationally for significant contributions to Markov process theory, having settled several open problems and conjectures, and mathematical modelling in population biology, ecology, economics, epidemiology, chemical kinetics, and telecommunications.
Phil holds an honours degree in Applied Mathematics from the University of Adelaide, and a PhD degree in Applied Probability from the University of Cambridge. He joined the University of Queensland Department of Mathematics in 1987 as Senior Lecturer, having previously held positions at the University of Adelaide, Murdoch University and the University of Wales College of Cardiff. He was promoted to Reader in 1993 and to Professor in 2004. He is a Fellow of the Australian Mathematical Society.
Phil's research has been supported by 12 ARC Large/Discovery/Linkage grants, and he was a Chief Investigator (2002-2014) within the ARC Centre of Excellence for Mathematics and Statistics of Complex Systems (MASCOS). In 1993 he was awarded the Moran Medal by the Australian Academy of Science for distinguished research in Applied Probability.
Phil served on the Australian Research Council (ARC) 2010 Excellence in Research for Australia (ERA) Research Evaluation Committee, and the ARC College of Experts 2013-15, 2018-21. He has served on the editorial boards of the Journal of the Australian Mathematical Society, Methodology and Computing in Applied Probability, Stochastic Models, and The Annals of Applied Probability, and has served on the organizing committees of several major international conferences. Phil devised the Probability Web, recognized as the main Web resource for probabilists throughout the world, and one of the first academic web sites. He has a strong record of innovation in undergraduate teaching, and has guided the development of many postgraduate students and postdoctoral fellows through supervision and collaboration.
Within ACEMS, Phil is Co-Leader of Theme 2 "Multiscale Models", and also works within Theme 4 "Informed Decisions".