MSc candidate in Applied Economics and Markets at the University of Bologna, focused on data analytics, financial risk, and fintech.
I build reproducible analytical workflows for financial and economic data, spanning fraud modeling, systematic market research, econometrics, and digital-asset risk.
- Hermes Crypto Lab — systematic-market research with public financial data, tested pipelines, execution safeguards, and research governance.
- IEEE-CIS Fraud Detection — XGBoost and imbalanced classification; the saved notebook records a held-out validation ROC-AUC of 0.942.
- Fraud Detection Streamlit App — a model-to-interface workflow for interactive fraud-classification predictions.
- Portfolio Theory — Asset Allocation — University of Bologna analysis of portfolio optimization, efficient frontiers, asset pricing, and Black–Litterman allocation.
- Investment Deflator: ARDL and Cointegrated VAR — time-series analysis of investment-deflator inflation using ARDL, cointegration, and VAR/VECM methods.
- Education and High-Skill Employment in the Netherlands — applied microeconometrics using IPUMS International 2011 data, OLS/logit models, and predicted margins.
- Data & ML: Python · pandas · scikit-learn · XGBoost · Streamlit · DuckDB
- Econometrics: Stata · time-series analysis · OLS/logit · cointegration
- Research practice: Jupyter · Git · automated tests · data and result provenance
Banking tokenization, digital-asset exposure, and financial-system risk.