We bring together data and models to solve complex combinatorial optimisation problems. By tackling real-world problems in socio-technical situations, we gain fundamental insights into the complementary interplay of machine learning and model-based search.

We design scalable computational models using micro-simulation, behavioural and spatial data, and participatory modelling, to understand the trade-offs of policy interventions, particularly in urban settings.

We recognise that decision making is ripe with uncertainty, and this uncertainty has multiple forms. By disentangling aleatoric and epistemic uncertainty, we develop understanding of how AI algorithms can be made more reliable.