Pablo studied Industrial Engineering at the Polytechnic University of Madrid (2013), and holds a master’s degree in Mechanical Engineering from Drexel University (2014). Despite his engineering background, Pablo focused on the study of fundamental transport phenomena with computational plasma physics for this doctoral degree, which he received in 2019 from MIT.
In his work, Pablo utilizes state-of-the-art machine learning techniques to solve complex physics and engineering optimization problems. He has been leading the efforts of predicting the performance of the SPARC tokamak, poised to be the first magnetic-confinement fusion device to study net-energy plasmas, and his papers on the topic are among the most read in Nuclear Fusion. Despite his young age, Pablo has authored over 30 peer-reviewed journal papers, a third of which as first author. He has been the recipient of important awards during his academic career, including the Manson Benedict Award and Del Favero Doctoral Thesis Prize at MIT, and was listed by Forbes magazine as part of their 30 Under 30 list in Science in 2021.
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