Senior Machine Learning Engineer @ Machine Learning Reply
Generative AI · LLMs · Computer Vision · Optimization
I'm a Machine Learning Engineer based in Italy, working at Machine Learning Reply (Turin), where I design and build AI solutions in:
- Generative AI & LLMs: adapting open-source LLMs to enterprise domains through Continual Pre-Training and Fine-Tuning, from data preprocessing and synthetic data generation to evaluation
- Computer Vision: design and development of Computer Vision solutions for enterprise use cases
- Logistics & Optimization: Machine Learning and optimization algorithms for intermodal rail and road transport planning and for the scheduling of container terminal operations
Before joining Reply I was a Researcher in Artificial Intelligence at the University of Bologna and University of Pisa, working on Generative AI and Trustworthiness.
| Role | Organization | Period |
|---|---|---|
| Senior Machine Learning Engineer | Machine Learning Reply, Turin | Oct 2026 – Present |
| Machine Learning Engineer | Machine Learning Reply, Turin | Dec 2024 – Oct 2026 |
| Researcher in Artificial Intelligence | University of Bologna and University of Pisa | Nov 2023 – Nov 2024 |
| Research Intern: adversarial attacks on explainability methods | University of Perugia | Sep 2022 – Jan 2023 |
- C. Di Maio, C. Cosci, M. Maggini, V. Poggioni, S. Melacci. Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI 2025), top-tier conference (CORE rank A), Frontiers in Artificial Intelligence and Applications, vol. 413, pp. 4041–4048.
- Speaker at several events on AI and AI applied to logistics
- Program Committee Member, 2nd AEQUITAS Workshop on Fairness and Bias in AI @ ECAI 2024
- Teaching Assistant, University of Perugia: Machine Learning with Laboratory (M.Sc., 2024) and Procedural Programming with Laboratory (B.Sc., 2022–2023)
- M.Sc. in Computer Science, Artificial Intelligence curriculum, University of Perugia (2021–2023), 110/110 cum laude
Thesis: Adversarial Attacks on Explainability Methods for Image Classification Models - B.Sc. in Computer Science, University of Perugia (2018–2021), 110/110 cum laude
Thesis: Bitcoin price-trend prediction using Machine Learning - Schools: ESSAI & ACAI 2024 (Athens), AI & Society Summer School 2024 (Tropea, Best Project Award), School in AI 2022 (UniMoRe), CyberChallenge 2020
| Project | Description | Topic |
|---|---|---|
| complex-network-link-prediction | Python library, published on PyPI, implementing the main link-prediction techniques for complex networks | Complex networks |
| Ant Colony Optimization for OSSP | Open Shop Scheduling Problem solved with Ant Colony Optimization | Optimization |
| GMRES | GMRES for the numerical solution of indefinite non-symmetric linear systems | Numerical analysis |
| BTC Dataset Generator | Builds a Bitcoin dataset with all the features available on Glassnode | Data |
| GAN Overview | Overview of GANs and their main variants, with implementations | Generative AI |
| LLMs Robustness Evaluation | Robustness evaluation of LLMs on sentiment analysis | LLMs |
| AOPC MoRF | Area Over Perturbation Curve (Most Relevant First) to evaluate XAI methods | Explainable AI |
- Generative AI: Continual Pre-Training, Fine-Tuning (LoRA), RAG, synthetic data generation
- Optimization: Operations Research, Constraint Programming with Google OR-Tools (CP-SAT)
- Libraries: PyTorch, TensorFlow, Keras, OpenCV, NumPy, SciPy, NetworkX, Pandas, Matplotlib, scikit-learn
- Cloud: AWS, GCP, Azure