Research

I am an applied mathematician studying how intelligence emerges in living systems. My research develops mechanistic models to explore how oscillations, feedback, and adaptive structure give rise to learning, coordination, and distributed information processing. By combining nonlinear dynamics, network theory, and computational modelling, I investigate mathematical principles underlying biological intelligence across evolutionary and developmental scales. A full list of publications is available on my Google Scholar profile and in my CV.

Non-neural and evolutionary perspectives on intelligence

I study how intelligent, adaptive behaviour can emerge in living systems without neurons. Using slime moulds and related minimal systems as conceptual testbeds, I develop mechanistic models in which oscillations, biochemical feedback, and morphology jointly shape information processing and decision-making. My goal is to understand how distributed coordination and learning-like behaviour can arise from local physical interactions, and what this reveals about the evolutionary origins of cognition.

Neural and developmental perspectives on intelligence

I also study how coordinated computation emerges in neural tissue. Using brain organoids as developmental model systems, I analyse how oscillations, network growth, and feedback give rise to structured activity and information flow. This work complements my evolutionary perspective by examining how intelligence reorganizes as neural circuits form.

Collective behaviour and spatial modelling

Many biological systems display large-scale coordination arising from local interactions. I use spatial models and nonlinear dynamics to analyse how feedback, coupling, and movement generate collective motion, pattern formation, and adaptive decision-making. This work established the conceptual foundation for my current research on biological intelligence, where similar principles operate across evolutionary and developmental contexts.

Modelling as a scientific practice

I study biological intelligence through mathematical models, but I also ask what it means to do mathematical modelling of biological systems, how the complexity of biology shapes the kinds of explanations such models can provide, and how this should guide modelling practice. More broadly, I contribute to the foundations of mathematical modelling, how we choose abstractions, how we interpret them, and where the limits of mechanistic models lie. This includes work on modelling pluralism in mathematical biology and ongoing projects on the epistemology of network methods, arguing that structural descriptions can be informative without automatically amounting to mechanism. I am also interested in the science of science, using bibliometric and network approaches to understand the role of mathematical models in biology and how interdisciplinary fields such as mathematical biology evolve.