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June 2020

Stochastic Modelling and Optimisation

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.

Matthew RoughanNetwork tomography and traffic matrix modelling, Routing robustness, Compressive sensing, Privacy preserving data mining and management

Nigel BeanMarkov chains, Matrix analytic methods, Operations research, Mathematical modelling

David GreenMarkov chains, Matrix analytic methods, Simulation modelling

Tony RobertsModelling stochastic dynamics

Joshua RossMathematical biology, Mathematical modelling, Markov chains, Operations research, Stochastic modelling