Our focus
We are modelers. We recognize that “all models are wrong, but some are useful,” a principle that underscores how simplified representations can provide valuable answers when their assumptions and limitations are made explicit and understood. This is the essence of physics: using abstractions, like the proverbial spherical cow, to isolate essential features, focus on what matters, and deliberately neglect what can be ignored. Effective modeling demands transparency from the modelers and awareness from the users.
Guided by this philosophy, our research focuses on theoretical and computational modeling of complex systems, with an emphasis on data-driven approaches and the analysis of collective behaviors. We work across epidemiology, ecology, systems biology, neuroscience, and social sciences, employing tools from statistical physics, nonlinear dynamics, network science, and big data analysis to uncover the mechanisms and interactions that drive real-world complex systems.
