Mattia Curri

Master's Degree Computer Science Student — Software Developer — AI Engineer
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About Me

Ciao! I'm Mattia, an Italian Computer Science bachelor graduate. I'm interested in expanding my cultural boundaries, improving my technical and professional knowledge. I am willing to work in the software development field, although I have little experience, I have a lot of interest and willingness to learn new things.

Education
Master's Degree in Computer Science — Artificial Intelligence Track
University of Bari
Current GPA: 4.0/4.0
  • Course entirely taught in English
  • Relevant courses: Numerical Methods, Database II, Formal Methods, Fundamentals of Artificial Intelligence, Natural Language Processing, Software Engineering for AI-Enabled Systems
Bachelor's Degree in Informatica (Computer Science)
University of Bari
110 with honours/110 (Final GPA: 3.8/4.0)
  • Thesis in Computational Intelligence: Supervised Learning Techniques for Semantic Segmentation of Aerial Images of Agricultural Fields
    • 3 Months research internship at the Cilab Lab under the supervision of Prof. Gennaro Vessio.
    • Comparison of Convolutional and Transformer architectures for the Agriculture-Vision Challenge at CVPR 2024, training models on ~50k images.
  • Relevant courses: Computational Intelligence, Algorithms and Data Structures, Databases, Software Engineering, Knowledge Engineering, Information Retrieval, Human-Computer Interaction, Computer Networks.
Projects
Code Comment Classification System — Team Project
Software Engineering for AI Exam Project
PythonCatBoostGrafanaFastAPIDockerMLflowPrometheusGitHub Actions
  • Architected a production-grade multi-label classification system categorizing code comments across Java, Python, and Pharo
  • Engineered end-to-end ML pipeline: data versioning (DVC) → automated preprocessing → dual-model training (CatBoost + SetFit) → experiment tracking (MLflow) → containerized deployment
  • Implemented comprehensive data quality framework improving dataset integrity by 12%
  • Deployed full-stack solution: FastAPI backend with async processing → Gradio frontend → Prometheus/Grafana monitoring, with sub-2s inference latency
  • Achieved 92.7% test coverage across unit, integration, and behavioral test suites
  • Published live demo on Hugging Face Spaces with automated CI/CD
EmPULIA Knowledge Graph-Based RAG for Question Answering — Solo Project
AI/NLP Project
PythonKnowledge GraphsRAGNeo4jFAISSTransformersOllama
  • Developed a comprehensive Retrieval-Augmented Generation system analyzing ~20 legal documents (up to 200 pages each)
  • Built end-to-end pipeline: web scraping → PDF processing → knowledge graph extraction with Gemini → semantic indexing → RAG inference
  • Implemented knowledge graph normalization using DBSCAN clustering and semantic embeddings to reduce entity redundancy
  • Created custom evaluation framework with Context Faithfulness, Context Precision/Recall, and Answer Accuracy metrics
  • Integrated multiple retrieval strategies: standard, multi-query, entity extraction, and Neo4j random walks
Temporal Link Prediction on Social Networks — Solo Project
BigData Exam Project
PythonPyTorchNetworkXWeights & Biases
  • Developed a temporal link prediction system using Evolving Graph Convolutional Networks (E-GCN) to forecast future social connections in the Gab network
  • Integrated BERT embeddings (768-dimensional) to capture semantic content from user posts and enhance prediction accuracy
  • Engineered a multi-snapshot temporal pipeline handling 6 historical time steps with incremental training and fine-tuning
  • Achieved MAP=0.87, Macro F1=0.86, AUC-ROC=0.80 on temporal link prediction tasks
Corporate Credit Rating Prediction — Solo Project
AI Project
PythonScikit-learnPandasPgmpyImblearnMatplotlibNumPy
  • Predicted ratings assigned by rating agencies to companies
  • Handled unbalanced dataset using Class Weights, SMOTE, ADASYN, SMOTETomek, SMOTEENN
  • Implemented a Bayesian Network for probabilistic queries, with Balanced Accuracy of 0.59
  • Achieved up to 86% Balanced Accuracy using XGBoost and Random Forest
Publications

A. Porcelli, F. Di Gravina, E. Fontana, M. Curri, F. D. Di Gregorio. “FFT-UniBa at Cruciverb-IT: Special Length Tokens and CSP for Italian Crossword Solving.” In EVALITA 2026, Pre-print: link

  • Fine-tuning of Italian T5 with length-constrained generation and CSP-based grid solver; MRR 0.63, 34% full grid accuracy.
Awards, Hackathons & Competitions
First Italian University AI Competition
AILLMOpen Data
  • Achieved 2nd place in the first stage, out of 20 teams.
  • Used open data to create a solution for the city of Bari, focused on logistics and intelligent traffic management.
ITAData Hack 2025
PythonCatBoostScikit-LearnImblearnPandas
  • Anomaly Detection task in Hadoop Distributed File System Logs.
  • Secured 2nd place out of 15 teams.
AI2B Hackathon Winner
CryptographyPrompt Engineering
  • Winning team of the AI2B Hackathon organized by University of Bari and AI2B, focused on AI and cybersecurity.
  • Obtained €500 winning first place out of 10 teams.
CyberChallenge 2024
WebBinary ExploitationCryptographyNetwork
  • Qualified to the national stage by ranking in the top 6 of the local venue, reaching 21st place out of 43 teams nationally.
  • Qualified for the local stage by being in the top 20 of the selection.
Additional

Languages: Italian (native), English (B2 Cambridge)