Exploring complexity through networks, computation, and data.
We build and analyse complex networks across social, urban, and informational systems to understand how their structure shapes dynamics, robustness, and emergent behaviour at multiple scales.
We develop agent-based, optimisation, and biologically inspired computational models to simulate and predict complex system dynamics — from crime patterns and misinformation spread to urban inequality and human mobility.
We apply machine learning, statistical inference, and AI-driven analysis to large-scale datasets, uncovering hidden patterns and emergent properties that support evidence-based understanding of complex social and urban systems.
Inequality is not random: it is encoded in the structure of cities, economies, and social networks. We develop computational and data-driven frameworks to map how inequality emerges, concentrates, and persists across space and time. From income and mobility disparities within urban environments to systemic gaps in access to education, healthcare, and opportunity, our work targets the mechanisms that drive inequality rather than merely measuring its outcomes. By modelling these mechanisms, we aim to give policymakers and communities the tools to intervene at the right points in complex systems.
Cities and natural environments are deeply interconnected systems whose structure shapes resilience, sustainability, and quality of life. We apply network analysis, spatial modelling, and large-scale data science to questions at this intersection. From how urban topology influences energy consumption and pollution exposure, to how climate pressures propagate through ecological and infrastructural networks, our goal is to move beyond description toward predictive understanding that can inform smarter and more equitable urban and environmental planning.
We live in an economy built on information: its creation, curation, and circulation shapes markets, politics, public health, and social cohesion. We use computational, network, and mathematical modelling to study how information and misinformation spread, how influence concentrates in digital ecosystems, and how collective behaviour emerges from billions of individual interactions. Understanding these dynamics is essential for designing platforms, institutions, and policies that are robust to manipulation and responsive to truth.