🎯 My Story

My journey in computer science started in high school, where I fell in love with programming and electronics. That curiosity led me to a B.Sc. in Electronic and Computer Engineering (2015–2019) at the University of Trieste, where my first AI courses sparked a passion that shaped everything that followed — continuing with a M.Sc. in Computer Engineering (2019–2022) focused on Data Science and AI. A semester at Montanuniversität Leoben through Erasmus+ pushed me out of my comfort zone: applying AI to energy and logistics taught me how transferable these methods really are.

For my Master's thesis I turned to computer vision applied to the humanities, working alongside art historians and digital humanities experts. It was my first real contact with academic research, and I loved every part of it — engaging with the state of the art, designing evaluations, and digging into how modern models actually work. The thesis was later published in IEEE Access (2023) and convinced me to stay on this road.

I continued with an Industrial Ph.D. (2023–2025), co-funded by PLUS S.r.l. within Area Science Park. I chose NLP — another field I had always loved — but my engineering mindset pulled me toward applied research useful for business. My thesis, Application of Large Language Models: Addressing Real-World Challenges, tackles three concrete questions: how to improve LLM outputs, how to evaluate them properly (with new metrics for Text-to-SQL), and how to make LLMs more sustainable through Green AI. Along the way I spent time as a visiting researcher at NOVA IMS in Lisbon and at UCL in London, attended 5 international summer and winter schools on NLP and ML, and published across journals, conferences and workshops.

I am a proactive person with a deep desire to learn and push beyond my comfort zone. I believe that great engineering and great research share the same foundation: understanding a problem deeply, building solutions methodically, and measuring impact rigorously.

🔬 Research Interests

My research is driven by a core question: how can we build AI systems that bridge the gap between human intent and structured information? Key areas include:

• NLP & Large Language Models — Improving and evaluating LLM outputs for structured tasks like code generation
• Text-to-SQL — Developing systems and metrics for natural language database querying
• Genetic Improvement of Code — Using evolutionary computation as a post-processing layer for LLM-generated code
• AI Coding Agents — Empirical analysis and optimization of AI agents for software development
• RAG & Retrieval Systems — Building intelligent agents that interact with complex data environments

🌍 International Experience

I believe the best research happens at the intersection of diverse perspectives: