We develop data-driven methods and mathematical models to study complex biological and physical systems, mostly defined through experimental dynamical data sets. These systems are typically nonlinear, multi-scale and chaotic, thus require new ideas to 1) best uncover the underlying causal mechanisms from their footprint on data, and 2) predict their behavior from the essential driving processes.
Morphogenesis & Active Matter
Coherent Structures
Flow Separation
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