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- Malgorzata O'Reilly
Assoc. Professor Malgorzata O'Reilly
Associate Investigator
University of Tasmania
Małgorzata received her MSc degree in Mathematics and Education from Wroclaw University in 1987. She completed her MSc thesis in the area of reliability of linear consecutive systems under the supervision of Prof Tomasz Rolski. She then received her PhD degree in applied probability from the University of Adelaide in 2002. Her PhD thesis supervisor was Prof Charles Pearce. Later, she completed her postdoc at the Teletraffic Research Centre at Adelaide University, working as a Research Associate on an ARC Discovery project, with Profs Peter Taylor and Nigel Bean, in the area of matrix-analytic methods.
She has been a lecturer in probability, operations research, stochastic modelling and discrete mathematics at the University of Tasmania since 2005.
Research Interests:
Applied probability
Healthcare Modelling
Matrix-analytic methods
Operations Research
Phylogenetics
Stochastic modelling
Qualifications:
PhD in Applied Probability
MSc in Mathematics & Education
Publications
Invited talks, refereed proceedings and other conference outputs
Samuelson, A., O'Reilly MM., & Bean N. G.
(2016). Classification of Generators for Stochastic Fluid Models: New Riccati equation for Psi.
The Ninth International Conference on Matrix-Analytic Methods in Stochastic Models.
O'Reilly, MM.
(2016). On mechanistic modelling of gene content evolution using Markov processes.
Phylomania 2016.
Bean, N. G., Nguyen G., O'Reilly MM., & Sunkara V.
(2016). A numerical framework for computing the limiting distribution of a stochastic fluid-fluid process.
The Ninth International Conference on Matrix-Analytic Methods in Stochastic Models.
Scheinhardt, W., & O'Reilly MM.
(2016). Analysis of tandem fluid queues.
The Ninth International Conference on Matrix-Analytic Methods in Stochastic Models.
Journal Articles
Samuelson, A., Haigh A., O'Reilly MM., & Bean N. G.
(2017). Stochastic model for maintenance in continuously deteriorating systems.
European Journal of Operational Research. 259(3), 1169-1179. doi: 10.1016/j.ejor.2016.11.032
Samuelson, A., Haigh A., O'Reilly MM., & Bean N. G.
(2017). Stochastic model for maintenance in continuously deteriorating systems.
European Journal of Operational Research. 259(3), 1169 - 1179. doi: 10.1016/j.ejor.2016.11.032
Stark, T. L., Liberles D. A., Holland B. R., & O'Reilly MM.
(2017). Analysis of a mechanistic Markov model for gene duplicates evolving under subfunctionalization.
BMC Evolutionary Biology. 17, 1-16. doi: 10.1186/s12862-016-0848-0
Samuelson, A., O'Reilly MM., & Bean N. G.
(2017). On the generalized reward generator for stochastic fluid models: A new equation for Ψ .
Stochastic Models. 1 - 29. doi: 10.1080/15326349.2017.1347883
O'Reilly, MM., & Scheinhardt W.
(2017). Stationary distributions for a class of Markov-modulated tandem fluid queues.
Stochastic Models. 33(4), 524 - 550. doi: 10.1080/15326349.2017.1349615
Anees, A., Aryal J., O'Reilly MM., Gale T. J., & Wardlaw T.
(2016). A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data.
ISPRS Journal of Photogrammetry and Remote Sensing. 122, 167-178. doi: 10.1016/j.isprsjprs.2016.10.011
Anees, A., Aryal J., O'Reilly MM., & Gale T. J.
(2016). A relative density ratio-based framework for detection of land cover changes in MODIS NDVI time series.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(8), 3359-3371. doi: 10.1109/JSTARS.2015.2428306
Margolius, B., & O'Reilly MM.
(2016). The analysis of cyclic stochastic fluid flows with time-varying transition rates.
Queueing Systems. 82(1-2), 43 - 73. doi: 10.1007/s11134-015-9456-8
Margolius, B., & O'Reilly MM.
(2016). The analysis of cyclic stochastic fluid flows with time-varying transition rates.
Queueing Systems. 82(1-2), 43-73. doi: 10.1007/s11134-015-9456-8
