Rosalinda earned a Bachelor’s Degree in Innovation Engineering for Digital Enterprises at the University of Palermo, developing skills in computer science and programming languages. She continued her studies by obtaining a Master’s Degree in Computer Engineering in March 2025, discussing an experimental thesis entitled “Graph Explainability for heterogeneous data”. During her thesis work, she trained different datasets: molecular, images and generalists, applying the HGE explainability technique and comparing the new model with existing models. The experimental thesis work allowed her to gain experience in machine learning and deep learning approaches applied to the field of biotechnology.

Within the molecular informatics group, Rosalinda works on the development of qualitative and quantitative approaches based on machine learning and deep learning for the prediction of biological and toxicological properties of xenobiotics. In particular, she is involved in the creation of the first platform capable of predicting binding properties of organic and inorganic ligands on quadruplex conformations of DNA and RNA. She is also involved in the development of predictive models aimed at studying the correlation between chemical structure and molecular reactivity in applications of toxicology (effects of air pollutants on human health) and drug design (design of inhibitors of the PCYOX1 enzyme).