- Home
- People
- Associate Investigators
- Pamela Burrage
Dr Pamela Burrage
Associate Investigator
Queensland University of Technology
Dr Pamela Burrage is a Senior Lecturer in the School of Mathematical Sciences at the Queensland University of Technology (QUT) and is also an Associate Investigator of ACEMS.
She received her PhD in Mathematics from the University of Queensland (UQ) in 1999, with the thesis “Runge-Kutta methods for Stochastic Differential Equations”.
After being a post-doctoral fellow at UQ in the departments of Mathematics and Civil Engineering, she became an Education/Research Fellow in the Queensland Parallel Supercomputing Foundation (QPSF) (2001 – 2006), developing educational materials and teaching in the areas of Visualisation and High Performance Computing.
A position as Senior Research Fellow at the Institute for Molecular Bioscience (UQ), 2007 – 2009, followed. This was with the QosCosGrid project, modeling and simulating in parallel (on a computational grid) the complex dynamical processes that take place on the plasma membrane of a cell.
She joined QUT as a Senior Lecturer in 2010 in the School of Electrical Engineering and Computer Science, and from 2013 has been in the School of Mathematical Sciences.
Research Interests:
Computational biology
differential equations
Mathematical modelling
stochastic differential equations
Visualisation
Qualifications:
MA (1st class Hons) (University of Auckland)
Doctor of Philosophy (University of Queensland)
Projects
Publications
Book Chapters
Burrage, K., Burrage P., Leier A., & Marquez-Lago T.
(2017). A Review of Stochastic and Delay Simulation Approaches in Both Time and Space in Computational Cell Biology.
(Holcman, D., Ed.).Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology. 241–261. doi: 10.1007/978-3-319-62627-7_11
Invited talks, refereed proceedings and other conference outputs
Lawson, B. A. J., Burrage K., Burrage P., Turner I., Santos R. W. dos, Jakes D., et al.
(2019). Mathematically Coping with Microfibrosis.
Mathematical and Computational Biology Seminar Series.
Burrage, P., Burrage K., & MacNamara S.
(2019). Integrated Approaches for Stochastic Chemical Kinetics.
17th International Conference of Numerical Analysis and Applied Mathematics.
Burrage, P.
(2019). Integrated Approaches for Stochastic Chemical Kinetics.
ANZIAM Conference 2019.
Lawson, B. A. J., Jakes D., Burrage K., Burrage P., Drovandi C. C., & Bueno-Orovio A.
(2019). Perlin noise for automatic generation of complex spatial patterns: an application to cardiac fibrosis.
ANZIAM Conference 2019.
Burrage, P., Donovan D., Thompson B., & Burrage K.
(2015). Populations of Models, Experimental Designs and Coverage of Parameter Space by Latin Hypercube and Orthogonal Sampling.
International Conference On Computational Science, ICCS 2015 - Computational Science at the Gates of Nature. doi: 10.1016/j.procs.2015.05.383
Burrage, K., Burrage P., Donovan D. M., McCourt T. A., & Thompson H. B.
(2014). Estimates on the Coverage of Parameter Space using Populations of Models.
Environment and Water Resource Management. 81-86. doi: 10.2316/P.2014.813-013
Journal Articles
Burrage, K., Burrage P., & MacNamara S.
(2020). The reflectionless properties of Toeplitz waves and Hankel waves: an analysis via Bessel functions.
arXiv. arXiv:2005.04561 .
Burrage, K., Burrage P., Turner I., & Zeng F.
(2018). On the Analysis of Mixed-Index Time Fractional Differential Equation Systems.
Axioms. 7(2), 25. doi: 10.3390/axioms7020025
Lawson, B. A. J., Burrage K., Burrage P., Drovandi C. C., & Bueno-Orovio A.
(2018). Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation.
Frontiers in Physiology. 9, 1114. doi: 10.3389/fphys.2018.01114
Lawson, B. A. J., Drovandi C. C., Cusimano N., Burrage P., Rodriguez B., & Burrage K.
(2018). Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology.
Science Advances. 4(1), e1701676. doi: 10.1126/sciadv.1701676
Donovan, D., Burrage K., Burrage P., McCourt TA., Thompson B., & Yazici EŞ.
(2018). Estimates of the coverage of parameter space by Latin Hypercube and Orthogonal Array-based sampling.
Applied Mathematical Modelling. 57, 553-564. doi: 10.1016/j.apm.2017.11.036
Abdullah, FA., Liu F., Burrage P., Burrage K., & Li T.
(2018). Novel analytical and numerical techniques for fractional temporal SEIR measles model.
Numerical Algorithms. 79(1), 19-40. doi: 10.1007/s11075-017-0426-6
Simmons, A., Burrage P., Nicolau D. V., Lakhani S. R., & Burrage K.
(2017). Environmental factors in breast cancer invasion: a mathematical modelling review.
Pathology. doi: 10.1016/j.pathol.2016.11.004
Psaltis, S. T. P., Farrell T., Burrage K., Burrage P., McCabe P., Moroney T. J., et al.
(2017). Using population of models to investigate and quantify gas production in a spatially heterogeneous coal seam gas field.
Applied Mathematical Modelling. 49, 338 - 353. doi: 10.1016/j.apm.2017.05.005
Drovandi, C. C., Cusimano N., Psaltis S. T. P., Lawson B. A. J., Pettitt A.N., Burrage P., et al.
(2016). Sampling methods for exploring between-subject variability in cardiac electrophysiology experiments.
Journal of The Royal Society Interface. 13(121), doi: 10.1098/rsif.2016.0214
Barrio, M., Burrage K., & Burrage P.
(2015). Stochastic linear multistep methods for the simulation of chemical kinetics.
The Journal of Chemical Physics. 142(6), 64101. doi: 10.1063/1.4907008
Psaltis, S. T. P., Farrell T., Burrage K., Burrage P., McCabe P., Moroney T. J., et al.
(2015). Mathematical modelling of gas production and compositional shift of a CSG (coal seam gas) field: Local model development.
Energy. 88, 621-635. doi: 10.1016/j.energy.2015.05.107
