🎓 MSc in Statistical and Actuarial Sciences — 110/110 cum laude — Università degli Studi del Sannio
📊 Data science, statistical modeling, and machine learning, with a focus on sports analytics and healthcare
🧪 Into Bayesian methods, deep learning, and reproducible research
📍 Based in Italy · open to roles across data science, ML/AI engineering, and research

• Multimodal architecture on the ISIC 2024 dataset (~401k images + 55 metadata variables): an EfficientNetB0 CNN on dermatoscopic images combined with FFNN / Random Forest / Gradient Boosting on tabular metadata
• Models stacked via a logistic-regression meta-learner using out-of-fold predictions to prevent leakage
• ROC-AUC ≈ 0.953; decision threshold selected via Youden's index and framed for clinical triage (minimizing false negatives), as decision support rather than diagnosis
• 📘 Paper on Zenodo

• Comparative study using logistic regression, LDA, bagging, random forest (with hyperparameter tuning), and feedforward neural networks
• SHAP explainability, ROC / Brier / F1 evaluation, full data pipeline, and reproducible code
• 📘 Paper on Zenodo · 📊 Dataset on Kaggle

• Monte Carlo simulator for the UEFA Swiss-system format, driven by a Bayesian Poisson model for match outcomes
• Estimates qualification and seeding probabilities across simulated tournament runs

📎 Download CV (PDF)

• ✉️ Email: mmorella9@gmail.com
• 💼 LinkedIn: matteo-morella
• 🧠 Zenodo: Skin Cancer · xG Model
• 🐱 GitHub: @mat126