Frontiers in Interacting Particle Systems, Aggregation-Diffusion Equations & Collective Behavior 

Frontières dans les équations de systèmes de particules en interaction. Equations d’agrégation-diffusion et comportement collectif

24 – 28 June 2024


Scientific Committee & Organizing Committee
Comité scientifique & Comité d’organisation

José A. Carrillo (University of Oxford)
Anne Nouri (Aix-Marseille Université)

The main objective of this school will be to bring together highly influential contributors in the field to date, covering various aspects of nonlinear and nonlocal aggregation-diffusion equations, with a focus on new applications in cellular biology, neuroscience, and machine learning. All these applications share a common denominator: mean-field limits and interacting particle systems.

This event echoes the research school organized the week after (July 1 – 5)  « Mathematical Biology: Collective Behavior and Pattern Formation« .
The interested participants may consider a coupled registration.



Pierre-Emmanuel Jabin (University of Penn-State)     The mean-field limit of non-exchangeable integrate and fire systems
Ansgar Jüngel (TU-Wien)    Multispecies populations: interacting particles, cross diffusion, and entropies
Anna Korba (ENSAE/ CREST)     Wasserstein gradient flows and applications to sampling in machine learning
Hideki Murakawa (Ryukoku University)     Mathematics of cell sorting: modelling, analysis and applications
Yao Yao (NUS, Singapore)     Steady states and dynamics of the aggregation-diffusion equation



Vincent Calvez (CNRS, Université Rennes)  Kinetic modeling of bacterial crowding
Matthias Merkel (CPT, Aix-Marseille UniversitéMechanisms of cellular unmixing in stem cell aggregates
Tâm Mignot (Laboratoire de Chimie Bactérienne, CNRS, Marseille)

Milica Tomasevic (Ecole Polytechnique)  Particle approximation of the doubly parabolic Keller-Segel equation in the plane
Ariane Trescases (Institut de Mathématiques de Toulouse)  On Keller-Segel type models for chemotactic aggregation with local sensing