- Home
- People
- Chief Investigators
- Phil Pollett
Professor Phil Pollett
Chief Investigator
The University of Queensland
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".
Research Interests:
Markov process theory
Mathematical modelling
Qualifications:
BSc (Hons) University of Adelaide 1979
PhD University of Cambridge 1983
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
Pollett, P., & Hodgkinson L.
(2021). Population networks with no occupancy ceiling.
ANZIAM 2021.
Pollett, P., & Hodgkinson L.
(2020). High-density limits for infinite occupancy processes.
64th Annual Meeting of the Australian Mathematical Society.
Pollett, P.
(2020). Health system demand and the spread of COVID-19.
ACEMS COVID-19 Research Workshop.
Pollett, P.
(2019). Quasi stationarity.
The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. 52.
Pollett, P.
(2019). Infinite-patch metapopulation models: branching, convergence and chaos.
6th Conference on Mathematical Models in Ecology and Evolution.
Pollett, P.
(2018). Infinite-patch metapopulation models: branching, convergence and chaos.
40th Conference on Stochastic Processes and their Applications.
McVinish, R., Barbour A. D., & Pollett P.
(2017). Local approximation of a metapopulation’s equilibrium.
Applied Probability @ The Rock.
Pollett, P.
(2017). Metapopulations in evolving landscapes.
61st Annual Meeting of the Australian Mathematical Society.
Pollett, P., McVinish R., & Chan J.
(2017). Metapopulations in evolving landscapes.
6th Conference on Mathematical Models in Ecology and Evolution (MMEE 2017).
Chan, Y.S., McVinish R., & Pollett P.
(2016). A metapopulation model incorporating landscape dynamics.
2nd International Conference on Current Progress in Mathematics and Sciences (ISCPMS).
Pollett, P.
(2016). Where are the bottlenecks?.
European Conference on Queueing Theory.
Journal Articles
Hodgkinson, L., McVinish R., & Pollett P.
(2020). Normal approximations for discrete-time occupancy processes.
Stochastic Processes and their Applications. 130(10), 6414 - 6444. doi: 10.1016/j.spa.2020.05.016
Barbour, A. D., McVinish R., & Pollett P.
(2018). Local approximation of a metapopulation’s equilibrium.
Journal of Mathematical Biology. 77(3), 765 - 793. doi: 10.1007/s00285-018-1231-0
McVinish, R., Pollett P., & Shausan A.
(2016). Limiting the spread of disease through altered migration patterns.
Journal of Theoretical Biology. 393, 60-66. doi: 10.1016/j.jtbi.2015.12.019
McVinish, R., Pollett P., & Chan Y. S.
(2016). A metapopulation model with Markovian landscape dynamics.
Theoretical Population Biology. 112, 80-96. doi: 10.1016/j.tpb.2016.08.005
Cope, R.C., Pollett P., Lanyon J.M., & Seddon J.M.
(2015). Indirect detection of genetic dispersal (movement and breeding events) through pedigree analysis of dugong populations in southern Queensland, Australia.
Biological Conservation. 181, 91-101. doi: 10.1016/j.biocon.2014.11.011
Barbour, A. D., McVinish R., & Pollett P.
(2015). Connecting deterministic and stochastic metapopulation models.
Journal of Mathematical Biology. 71(6-7), 1481-1504. doi: 10.1007/s00285-015-0865-4
Cope, R. C., Lanyon J.M., Seddon J. M., & Pollett P.
(2014). Development and testing of a genetic marker-based pedigree reconstruction system ‘PR-genie’ incorporating size-class data.
Molecular Ecology Resources. 14(4), 857-870. doi: 10.1111/1755-0998.12219
