We are a multidisciplinary research group dedicated to understanding the structure and dynamics of complex systems. Our approach integrates theoretical methods from fields such as statistical physics, nonlinear dynamics, differential geometry, statistical inference, and deep learning. We collaborate closely with other research groups, particularly experimental labs that generate data from real-world systems such as brains, mobility networks, and ecosystems.

Current research topics include:

• Embedding complex networks in hyperbolic space
• Determining how the brain’s shape influences patterns of neuronal activity
• Analyzing symmetries and dynamical properties of complex systems using Koopman operator theory
• Studying the stability of ecosystems and detecting critical species from co-occurrence patterns in ecological data
• Investigating the fundamental limits of network reconstruction from observational data using information-theoretic approaches