I hold a Bachelor's degree in Bioinformatics from the National University of Entre Ríos (UNER), Argentina (2014), a Ph.D. in Computer Science from the National University of the South (UNS), Argentina (2020), and a Specialization in Data Science from the same institution (2023). I have been a researcher at the National Scientific and Technical Research Council (CONICET), Argentina, since 2022, and a member of the National System of Researchers (SNI), Uruguay, since 2025.

My research interests lie at the intersection of artificial intelligence, cheminformatics, and explainable AI (XAI) methods. I am particularly interested in exploring the limits and potential of using synthetic libraries to train machine learning models, especially for molecular property prediction and chemical compound design tasks. I also investigate how generative models—such as those based on SMILES representations—can contribute to the creation of more efficient and representative datasets. A complementary line of work focuses on developing predictive models for the design of new materials, particularly polymers, where structural complexity and chemical diversity pose unique challenges. In all cases, I aim to incorporate explainability approaches to assess model behavior and robustness, with the goal of advancing toward more reliable, interpretable, and practically useful solutions for both science and industry.