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Stochastic Modelling and Operations Research

Much of human intellectual endeavour is directed to predicting and modifying the future. Initially this is attempted by looking for patterns. Sophisticated methods for the study of patterns are provided by mathematics. Measurements and data are studied to suggest patterns, which are then modelled mathematically. The predictions of the models are tested against further observations and the models used to make decisions and modify future outcomes.

The applications of stochastic, or random, processes in engineering, finance, biology and many other fields fit this paradigm.

In all cases real world measurements and data provide the foundations for theoretical models. These models are then explored to make predictions and assist in improved decisions, whether they are investment strategies, management policies in telecommunications networks, or potential new avenues for cancer treatment.

We are associated with two groups that specialise in performing contract research and consulting with the defence sector; CDCIN, and the telecommunications sector and general industry, TRC Mathematical Modelling.

Researcher and Interests
Nigel Bean Matrix-analytic methods in stochastic modelling, Markov chains, stochastic modelling; network modelling; operations research; mathematical modelling
Andrew Black Stochastic epidemic modelling, evolutionary dynamics, complex systems and stochastic modelling
Robert Cope
David Green Matrix Analytic Methods, Markov chains, Hidden Markov models, Optimisation, Stochastic Dynamic Programming and Simulation Modelling
Giang Nguyen Matrix-analytic methods, stochastic fluid processes, Brownian motion
Joshua Ross Mathematical biology, mathematical modelling, Markov chains, operations research, stochastic modelling
Matthew Roughan Network tomography and traffic matrix modelling, routing robustness, compressive sensing, privacy preserving data mining and management
Andrew Smith Stochastic processes and dynamical systems applied to populations and more specifically metapopulations
Silvio Tarca
School of Mathematical Sciences
Level 6, Ingkarni Wardli

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