Interaction laws to collective behaviour: Inferring population dynamics

Organizers:

Rebecca Crossley, Stéphanie Abo (University of Oxford), University of Oxford

Description:

This mini-symposium brings together experts from around the globe who are developing cutting-edge techniques for decoding collective behaviour in complex systems, from cellular dynamics to social phenomena. We focus on techniques such as inferring interaction laws from noisy data, designing novel neural network architectures to model emergent behaviour, and applying information-theoretic approaches to understand collective decision-making. The challenge lies not just in quantifying how local interactions aggregate to produce tissue-level or network-level behaviours, but in developing data-driven mathematical frameworks that bridge these scales while preserving essential features. Speakers will present methodological advances in population modelling and statistical learning alongside practical applications in biological systems, ranging from tissue patterning to organised motion. By uniting diverse perspectives, we aim to bridge theory and experiment, foster collaboration, and extract valuable biological insights into how local interaction rules drive coordinated population dynamics.

Diversity Statement:

To explore how local rules give rise to emergent behaviour, we have brought together researchers who approach this fundamental question through complementary lenses: mathematical modelling, statistical inference, and information theory. Our speakers reflect diverse backgrounds, career stages, and institutions worldwide, with a thoughtful balance of gender representation. This intentionally structured event will foster an interdisciplinary dialogue to advance research in this field.