Daniel Rodrigues Perazzo

Studying dynamical systems is central to both mathematics and computer science. These systems model how state variables evolve over time and underpin applications from molecular dynamics to climate. Recent advances in operator learning, especially within the Koopman operator framework, offer new ways to learn dynamics from data. I will develop and apply these methods to climate phenomena such as temperature and circulation patterns, combining theoretical work with practical modeling. In the same direction, I will also study causality and uncertainty quantification in this setting.