MSc candidate in Applied Economics and Markets at the University of Bologna, focused on financial risk and data analytics.
I apply Python and quantitative methods to financial and economic questions, building reproducible workflows alongside academic work in quantitative finance and econometrics.
- Hermes Crypto Lab — paper-only systematic crypto research demonstrating public-data engineering, testing, governance, auditability, and execution safeguards.
- IEEE-CIS Fraud Detection — reproducible XGBoost workflow for transaction-fraud classification, with training-only preprocessing and imbalanced-classification evaluation.
- Fraud Detection Streamlit Demo — interactive model-to-interface demonstration connecting a saved scikit-learn pipeline to transaction predictions.
- Portfolio Theory and Asset Allocation — archival collaborative report covering mean–variance allocation, efficient frontiers, and Black–Litterman; original code and data are unavailable. Errata.
- Investment Deflator Econometrics — archival collaborative time-series report using ARDL, cointegration, and VAR analysis; source code and data are unavailable. Errata.
- Education and High-Skill Employment — archival collaborative, cross-sectional Stata/IPUMS report using LPM, logit, and predictive margins; the source workflow and microdata are unavailable. Errata.
Python · pandas · scikit-learn · XGBoost · Streamlit · DuckDB · Stata · Jupyter · Git · automated testing · data/result provenance · OLS/logit · time-series econometrics · cointegration · portfolio optimization
Banking tokenization, digital-asset exposure, and financial-system risk.

