My foundation is quantitative and technical: a BSc in Mathematics and graduate-level coursework in Data Science, both with top marks, combined with hands-on software engineering across backend systems, data pipelines, and applied research. I use that dual lens — rigorous analysis and working code — on everything I build.
I attended a Master's degree course in Data Science and Economics, scoring a perfect GPA of 30/30. Dropped out before the thesis to pursue my startup. Coursework covered Machine Learning, Statistical Learning, Econometrics, and Programming. Capstone project: "Decomposing and Clustering the EU Business Economy", applying dimensionality reduction and unsupervised clustering to large-scale macroeconomic datasets to identify structural patterns across industries.
Read the PublicationUndergraduate degree in Mathematics (GPA: 108/110) and a merit scholarship. This three-year degree (one of the University's hardest, with a 60% dropout rate) was a crucible for rigorous thinking and problem-solving. With a focus on deep theoretical foundations (Algebra, Calculus, Geometry) and applied subjects (Combinatorics, Physics, Optimization), it trained a way of thinking I rely on every day: start from a hypothesis and apply rigorous logic until it either collapses or brings you to a surprising insight. Keep your findings, and iterate.
In 2020, I co-authored an independent research project combining an app and scientific paper to provide data-driven insights into the COVID-19 pandemic. The project, titled "disCOVIDer19 - A path-guide inside the COVID-19 pandemia", analyzed epidemiological datasets and built visualizations to help users understand transmission patterns and regional dynamics. The project was recognized by the Italian Society of Statistics (SIS).
Read the Paper