Our research sits at the intersection of theoretical physics, computational engineering, and quantitative biology. We build mathematical models to uncover the fundamental principles that govern how tissues self-organize and acquire shape, in both embryos and in vitro systems such as organoids.

To this end, we develop a range of models: dynamical-systems approaches to explain cell differentiation and tissue patterning, and active-matter frameworks to describe the mechanics of cells and tissues. Because these problems typically involve nonlinear interactions in complex geometries, analytical solutions are often insufficient. We therefore design numerical methods and high-performance codes to explore model behavior beyond simple linearized cases.

Such computational approaches are essential not only for solving biologically realistic models, but also for comparing theoretical predictions with experiments, which frequently involve intricate geometries and topologies that resist purely analytical treatment.