About me

I’m currently a postdoctoral scholar working with Steve Brunton at the AI Institute in Dynamical Systems at University of Washington, specializing in data-driven modeling and control of complex multiphysical systems. My research primarily focuses on data-driven model discovery, the study of irregular geometries, and the application of machine learning to streamline complex optimization tasks in engineering.

I graduated with a bachelor’s degree in Energy Engineering from the University of Vigo (2019) and a master’s degree in Industrial Mathematics from a consortium of institutions -UVigo (Vigo), UDC (A Coruña), USC (Santiago de Compostela), UPM (Madrid) and UC3M (Madrid)- in 2021. In 2024, I completed my Ph.D. working in the Energy Technology Group (GTE) at the University of Vigo, focusing on data-driven surrogate models for optimizing enhanced heat transfer applications.

Throughout my academic journey, I’ve always looked for opportunities to learn more by collaborating with researchers from all over the world! During my undergrad, I spent an entire year at the Norwegian University of Science and Technology, where I worked under the supervision of Armin Hafner and Yosr Allouche. There, I contributed on the development of a cold thermal energy storage, integrated into a R744 supermarket refrigeration system. Later, during my Ph.D. studies, I also completed a three-month research stay at the University of Melbourne (Australia)., where I collaborated with Richard Sandberg’s group. There, I contributed to the development of surrogate models for microfinned surfaces using the group’s in-house machine learning framework, EVE3, based on evolutionary algorithms. You can find the results of this collaboration in my publications!