Pablo is an energy engineer specializing in computational fluid dynamics (CFD) within the wind energy sector. He began his PhD research by applying machine learning methods, particularly Approximate Bayesian Computation (ABC) with Monte Carlo Markov Chains (MCMC), to calibrate large eddy simulations (LES). His work later expanded into atmospheric modeling, where he studied the interactions between global wind patterns, continental land masses, and the Coriolis effect for educational and visualization purposes.

Currently, Pablo is involved in wind farm simulations, focusing on how turbine wakes interact to optimize farm layouts and improve yaw control systems. He is also working on developing a simplified real-time fluid dynamics solver for educational use. Looking ahead, Pablo is eager to focus on more accurate designs and contribute to innovative solutions that enhance the efficiency and sustainability of wind energy systems.