Roberto Paoli's background is in theoretical and computational fluid dynamics and his research interests span all areas that require multi scale complex flow modeling such as turbulent multiphase flows, reactive flows, atmospheric flows and flows in thermochemical non-equilibrium. He is also interested developing and using machine learning algorithms, particularly those embedding physical constrains and data assimilation. He uses extensively high-performance computing to develop in-house codes and to fine-tune large- scale models on massively parallel infrastructures
