Complex systems

Our research in complex systems has a strong focus on theory and quantitative methods covering the areas of guided self-organisation, large-scale complex networks and adaptive systems. We produce new methods for understanding complex systems based on information theory, mathematical sociology, machine learning, graph theory, computational epidemiology, agent-based simulation, dynamic systems theory, and systems biology.

This research leverages our expertise in engineering and computational sciences and involves collaborations across physics, mathematics, biology and social sciences. Research outcomes have an impact on diverse areas such as disaster and emergency management, large-scale epidemic modelling, organisational and social risk management, financial crisis forecasting, as well as the stability of power grids, communication and transport systems.