Research Engineer specializing in merging data-driven models with physical insights to solve complex industrial challenges. Curiosity drives me to constantly learn and explore new ideas. I am passionate about creativity, interdisciplinary learning, and bridging the gap between academic research and practical, real-world applications.

During my Master's thesis, I worked on data assimilation using physics-constrained optimization algorithms, laying the foundation for my interest in combining data-driven approaches with physical modeling. Later, during my PhD, I focused on analytically deriving reduced models and numerically solving partial differential equations to understand soft-matter physics. This work provided me with a solid background in tackling complex physical systems using both theoretical and computational methods, including the tools of statistical physics and applied math. Following my PhD, I worked at the Alan Turing Institute, where I focused on Bayesian ML, uncertainty quantification, and numerical simulations, including building a custom Gaussian Process Regression library to address specific needs of our industrial partners.

My experience spans the intersection of AI, machine learning, and physical modeling, with hands-on expertise in state estimation, computational physics, and Bayesian machine learning.