I was born and raised in Vigo (Galicia, Spain). My early interest in sustainable energy technologies inspired me to study Energy Engineering at the University of Vigo. I was always interested in exploring new cultures and academic environments, which led me to study my third and fourth years abroad: at the University of Knoxville (as part of the ISEP program), and at the Norwegian University of Science and Technology (with Erasmus+). These experiences greatly incentivized my interest in research. Particularly, in Norway I did my bachelor thesis on sustainable refrigeration systems, collaborating on the European project Super Smart-Rack.
I moved to the UK in 2018 to study the MPhil in Energy Technologies at University of Cambridge with a postgraduate fellowship from the Barrié Foundation from Galicia. I returned to Cambridge in 2020 to pursue a Ph.D. in Engineering. My doctoral research focused on developing data assimilation tools–the combination of experimental data and physical models–to achieve real-time accurate models of low-emission gas turbines. Upon completing my Ph.D. in 2024, I joined Imperial College London as a Research Associate in scientific machine learning.
Today, I am an Assistant Professor in the Department of Aeronautics at Imperial College London, where I also hold the Eric & Wendy Schmidt AI in Science Fellowship at I-X. My primary research specializes in developing real-time digital twins for complex engineering systems, with a focus on applying scientific machine learning to enhance monitoring, prediction, and control. Specifically, I develop methods to improve the understanding and management of nonlinear and chaotic phenomena, such as thermoacoustic instabilities in low-emission aeroengines or aeroelastic instabilities in wind turbines.
