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.