About

I am a fourth-year PhD candidate at the Escola Politécnica of the University of São Paulo, where my work sits at the intersection of functional analysis, partial differential equations, numerical methods, and machine learning. I am also Partner and Lead Data & AI Engineer at LyfeOS, and I teach Computational Mechanics at Insper.

My research is grounded in mathematics — particularly Banach-space methods, variational analysis, and the study of well-posedness, stability, and approximation mechanisms for PDE models. I am especially interested in how analytical insight can inform the design and interpretation of computational methods, including the Virtual Element Method and learning-based approaches for PDEs.

More broadly, I am drawn to questions where rigorous mathematical structure meets modern computation. This includes numerical analysis, scientific machine learning, and artificial intelligence — especially topics such as inductive bias, generalization, and principled training objectives. Across my academic, teaching, and engineering work, I aim to connect theory and implementation in a way that is both precise and useful.

GitHub · LinkedIn · Lattes (CNPq) · papers on the Publications page.