Data Science & Management · LUISS Guido Carli, Rome
I build retrieval and machine-learning systems — and I publish the numbers, including the ones that don't flatter me.
MSc candidate in Data Science & Management, following a BSc in Management & Computer Science graded 107/110. Most of what I build is a pipeline of some kind — retrieval, inference, econometrics — and the part I care about is whether the result survives scrutiny: an evaluation you can trust, limitations stated before someone else finds them, and a repo another person can actually run.
Currently open to internships and job roles alongside study.
| DiscoverAI with Deloitte · 2026 |
Review-aware hybrid search over 60K Amazon products. RRF-fused dense + BM25 retrieval in Qdrant, cross-encoder reranking, schema-enforced LLM summarisation. P@5 = 0.952 · nDCG@10 = 0.951 · 100% schema validity |
| AlgoWatch with Accenture · 2026 |
A Gemini-based agent auditing public-sector algorithms against the EU AI Act — ingest, evaluate, explain, remediate. 2nd place of ~150 projects · ~1,500 participants |
| Internet & Network Economics 2026 |
Can stablecoins replace SWIFT? Four hypotheses over 2020–2025, using the FTX collapse as a structural break. Log-log OLS with Newey–West errors · two-way fixed-effects panel regression |
| Thirsty Machines 2026 |
The energy, water and carbon cost of a single LLM query, mapped across data centres and grids. 66 models · 37 cloud regions · 6 Tableau dashboards + D3.js |
| CVE-2025-23211 2026 |
Reproducing a Jinja2 sandbox escape end to end — template injection to root RCE, then verifying the patch. CVSS 9.9 · Dockerised · exploit and fix both demonstrated |
| Synaptic Dialogues 2026 |
A week of my own AI usage, self-tracked and rendered as a navigable spatial topography rather than a bar chart. Three.js · deployed |
| Kalib personal · 2026 |
Built in my own time: a local-first nutrition PWA that measures maintenance calories from weigh-ins and logged intake instead of assuming a formula. Offline USDA database, 13k foods · measured TDEE with a 95% interval · 226 unit tests · live |
Evaluation before enthusiasm. A number without a baseline is decoration. Every system I build gets compared against the simpler thing it was supposed to beat — sometimes the simpler thing wins, and that is worth knowing early.
Limitations in the abstract, not the appendix. DiscoverAI's cross-encoder reranking lowers our headline metric, and the faithfulness audit came in under the threshold we set in advance. Both are in the first paragraph of the report. Work that hides its weak points is harder to trust than work that names them.
Reproducible or it didn't happen. Pinned model SHAs, fixed seeds, per-stage validation gates. Same seed and same hardware, same artefacts — otherwise a result is an anecdote.
MSc Data Science & Management — LUISS Guido Carli, Rome · 2025 – 2027
BSc Management & Computer Science — LUISS Guido Carli, Rome · 2022 – 2025 · 107/110
Thesis: The Impact of Artificial Intelligence on OSINT Technologies
Cisco Cybersecurity · Celonis Build Analyses · LUISS AI Literacy