This course provides doctoral students the foundations of applied probability and stochastic modeling. The first part of the course covers basic concepts in probability, such as the Borel Cantelli ...

Stochastic control and decision processes study the formulation and solution of decision-making problems under uncertainty, typically modelled by stochastic dynamical systems. At the heart of this ...

Randomness is inherent to real world problems so faculty research in this area includes the development and application of probabilistic tools to model, predict, and analyze randomness in applications ...

Systematic study of Markov chains and some of the simpler Markov processes including renewal theory, limit theorems for Markov chains, branching processes, queuing theory, birth and death processes, ...

Analysis and implementation of numerical methods for random processes: random number generators, Monte Carlo methods, Markov chains, stochastic differential equations, and applications. Recommended ...

Composite materials, characterised by their heterogeneous architecture, present significant challenges in predicting dynamic behaviour under real-world conditions. Stochastic analysis integrates ...