Beyond the CV
Some things don't fit in a curriculum — the people I work with, the things I'm building, and the places the work takes me.
The experiment
Legend has it I am the result of a long-running laboratory experiment conducted by Prof. Domenico Cotroneo and Prof. Roberto Natella, somewhere in the basement of the DESSERT Lab.
For years they applied every fault injection technique they had at their disposal — deadlines, rejections, conference reviews at 3 AM, paper #4 of the month, PhD defenses, missing GPUs, broken pipelines, and the occasional Reviewer 2. A full campaign.
Against all odds, the subject exhibited remarkable resilience: no crashes, no silent failures, and an unusually fast recovery time. The experiment was therefore declared a success and the prototype was deployed in production — first as an RTD-A, and now running in continuous integration at DIETI.
Residual failure mode: a deep, possibly unhealthy, passion for finding ways to break systems — especially AI. It turns out the experiment never really ended; it just moved from the lab to the paper track.
People I work with
Research is never a solo effort. Over the years I have been lucky to build long-standing collaborations with researchers whose work I admire — and, more importantly, whose company I enjoy.
Additional active collaborations include Prof. Yulei Sui (UNSW, Australia) on vulnerability detection in LLM-generated code — we recently published together in IEEE TSE — and Prof. Naghmeh Ramezani Ivaki (University of Coimbra, Portugal) on software security and automated detection of insecure AI-generated code.
Redlyne — Security for AI-generated code
Redlyne is a VS Code extension that detects vulnerabilities in AI-generated Python code and proposes one-click patches — directly in the editor, without ever sending code to a server.
It is the natural continuation of my research line on the security of AI-generated code: instead of stopping at the paper, the detection logic ships as a tool developers can actually install and use while they code. Everything runs locally — no code, no telemetry, no metadata leaves the machine.
You can install it from the VS Code Marketplace or browse the source on GitHub.
ARGO — AI for healthcare
Alongside my academic work, I am co-founder of ARGO, a startup founded together with Nefrocenter to bring AI for healthcare from the research bench to real clinical practice.
ARGO operates at the intersection of machine learning, software reliability, and medical decision support — building tools that help clinicians make better decisions, faster, without compromising on safety, explainability, or trust. We work on real data, with real doctors, on problems that actually matter to patients.
We're hiring
If you are a student, researcher, or engineer interested in AI for healthcare, clinical decision support, or trustworthy ML, we are actively looking for new profiles to join the team — both for research collaborations and for permanent positions.
Possible backgrounds: machine learning, data engineering, medical informatics, software engineering, or any field where you have built something real. If you are curious, get in touch with a short note about what you want to work on.
innovIT — San Francisco
In January 2026, I was selected by the Italian Innovation and Culture Hub (innovIT) to present DeVAIC — our tool for security assessment of AI-generated code — in San Francisco.
innovIT is the Italian government's outpost in the Bay Area, created to connect Italian research, startups, and industry with the Silicon Valley ecosystem. Being selected meant a week of talks, meetings, and conversations with investors, researchers, and engineers based in the Bay.
This story, I am fairly sure, will have a sequel.
Guiding students
A large part of what I do is not writing papers — it is being available to the young people who are just starting. I supervise theses at Università di Napoli Federico II, I mentor members of the CyberChallenge.IT team as local coordinator, I teach cadets at the Italian Air Force Academy (Accademia Aeronautica) in the Practical Cybersecurity course, and I work closely with the engineers and students joining the ARGO team.
These are very different audiences — undergraduates, master students, CTF players, cadets, junior engineers — but the goal is always the same: help them become independent, ask better questions, break things on purpose, and build something they are proud of.
I also believe strongly in open science: code, datasets, and trained models are released on GitHub (DESSERT Lab) and Hugging Face (OSS-forge). If the work is reproducible, the field gets better faster.