Curriculum Vitae

Research Interests

I am interested in AI safety and mechanistic interpretability. To me, interpretability is not an academic exercise for its own sake, but a pragmatic tool to make model safety measurable and verifiable on the path toward safe AGI. Right now I am particularly focused on the geometry of representations: how abstract concepts — like refusal and truth — are encoded within a model’s activation space.

Two questions follow from this:

1. Model biology: whether such concepts conform strictly to single linear directions, or rely instead on higher-dimensional, possibly non-linear manifolds.
2. Applied interpretability: whether representation-level edits — steering, abliteration, concept erasure — are genuinely surgical when evaluated against practical baselines, with off-target side-effects measured as rigorously as the primary behavioral change.

The two are connected, since an intervention built on the wrong picture of the geometry is precisely one whose side-effects go unmeasured.

Education

M.Sc. in Computer Science (Laurea Magistrale in Informatica, LM-18)
University of Milan, Italy
Sep 2024 – Present

• Thesis: The Geometry of Absence, Opposite, and the Activation Manifold in Representation-Level Interventions on LLMs
• Advisors: Prof. Alfio Ferrara & Elisabetta Rocchetti (University of Milan)
• Focus: Investigating representation-level interventions (linear concept erasure, steering) and studying how models encode the absence of a concept versus its opposite on the activation manifold.
• Expected defense: Summer 2027 (July 2027)
• Weighted average: 29.13/30 (3.88/4.00)
• Selected coursework: Natural Language Processing (30 cum laude), Distributed and Pervasive Systems (30 cum laude), Security of Service-Oriented Architectures (30 cum laude), Advanced Biometrics (30), Audio Pattern Recognition (30), Privacy and Data Protection (29)

B.Sc. in Computer Science (Laurea in Informatica, L-31)
University of Milan, Italy
Sep 2020 – Oct 2024

• Final grade: 110/110 cum laude; weighted average: 28.55/30 (3.81/4.00)
• Thesis: Design and development of a donation-tracking system for RECUP APS (Sviluppo di uno strumento di supporto all’associazione Recup)
• Advisor: Prof. Carlo Maria Bellettini
• Mathematical and theoretical foundations: Discrete Mathematics (30 cum laude), Mathematical Logic (30 cum laude), Automata and Formal Languages (30 cum laude), Programming Languages (30 cum laude), Artificial Intelligence I (30 cum laude), Calculus (30), Statistics and Data Analysis (30), Algorithms and Data Structures (30)

Diploma, Computer Science (Perito Informatico)
ITT “G. e M. Montani”, Fermo, Italy
Sep 2015 – Jul 2020

• Final grade: 100/100 cum laude
• Focus on Programming and Computer Networks

Research Experience

Independent Research — Representation Geometry of Truth
ISLab, University of Milan (Sole author; Natural Language Processing course)
Dec 2025 – Jul 2026

• Tested whether propositional truth is mediated by one linear direction or by a $k$-dimensional concept cone. Adapted form Wollschlager et. al (2025), TDO and TCO: gradient-optimised directions and cones under causal necessity, sufficiency and KL-retention losses.
• Result: A single direction is insufficient for the larger models, and a low-dimensional cone ($k=2$) is the better model of the concept. Its basis is near-orthogonal to the Difference-in-Means probe yet individually causally effective.
• Manuscript: The Multidimensional Geometry of Truth in LLMs: A Concept-Cone Extension of the Linear Representation Hypothesis (2026). [Draft] [Code]
• Techniques: activation patching, directional ablation/addition, Difference-in-Means probes, nnsight, PyTorch; single-GPU (A40).

Research Project — What a Heart-Sound Classifier Actually Learns
University of Milan (Sole author; Audio Pattern Recognition course)
Apr 2026 – Jul 2026

• Compared three feature representations (MFCC, log-Mel, perceptual wavelet packet) against three classifier families (SVM, Random Forest, CNN) on PhysioNet/CinC 2016 under one recording-level protocol, pairing every performance claim with a diagnostic capable of refuting it.
• Result: The headline MAcc of 0.866 is largely a recording-site artefact — site recoverable from the same features at 0.647 against 0.167 chance, diagnosis collapsing from 0.840 to 0.457 under leave-one-site-out. Attribution leans systolic, but no more so on correct than on incorrect predictions.
• Manuscript: What Does a Heart Sound Classifier Actually Learn? (2026). [Draft] [Code]

Research Project — Empirical Test of the Legends Hypothesis
SESAR Lab, University of Milan (With F. Bylyshi & H. El-Khazri, equal contribution)
Feb 2026 – Jun 2026

• Tested whether a model learns a regulation better from stories showing it being followed than from the regulation itself. Built a pipeline producing training corpora matched on everything but content, and GenderEqGLUE, a five-task benchmark evaluated on documents held out from training.
• Result: The two regimes teach complementary competences — training on the rule wins overall, training on the stories wins on the tasks that require applying it.
• Manuscript: The Legend Challenge: Embedding Ethical Compliance into LLMs through Champion Narratives (2026). [Draft] [Code]

Teaching Experience

Teaching Assistant — Accertamento delle Competenze Informatiche (3 CFU)
University of Milan (Scientific lead: Prof. Stefano Montanelli; delivered by CTU)
Jan 2026 – Present

• Prepared and delivered lectures to classes of students, authoring the accompanying teaching material.
• Led lab sessions of a blended-learning course shared across numerous undergraduate degree programmes, and invigilated examinations.

Engineering Experience

Lead Developer / Consultant
RECUP APS — University of Milan
Jan 2024 – Dec 2025

• Designed and built a donation-tracking web platform (React + TypeScript, Supabase DB/Auth/Storage, Netlify Functions); began as B.Sc. thesis, continued as a consultancy with me as lead developer.
• RecupGPT (2025): Retrieval-augmented natural-language querying over the production database (LangChain, LangGraph, NL2SQL with query rewriting and routing).

Full-Stack Developer Consultant
Value Process Srl, Milan
Jun 2022 – Sep 2022

• Developed microservices architecture in Spring Boot and React; led migration of a legacy system to a serverless architecture.

Full Stack Developer Intern
PC & Informatica SRL
Apr 2019 – Jun 2019

• Created responsive front-end interfaces; optimized SQL queries improving performance by 40% (PHP, JavaScript, MySQL).

Fellowships, Awards, and Honours

• Full academic + merit scholarships (tuition and housing), University of Milan (Sep 2020 – Sep 2026)
• Lead the Future — STEM mentorship community, Mentee (Sep 2025 – Present)
• BOOST ‘24 — Selected for the Bologna Orthogonal Summer Term for outstanding B.Sc./M.Sc. CS students (Aug 2024)
• Futuro Annunciato ‘24 — Selected for the event, for outstanding students (Jul 2024)
• Nomination, “Alfieri del Lavoro” Award — Presidency of the Italian Republic (Jul 2020)
• Participant, National Olympiads in Informatics & Mathematics (2017 – 2020)

Technical Skills

• Mechanistic Interpretability: Denoising activation patching, directional ablation and activation addition, Difference-in-Means probes, gradient-based direction/cone optimisation, concept erasure (INLP, LEACE), activation steering
• Post-hoc XAI: SHAP (tree and kernel estimators), Grad-CAM with saliency sanity checks, structured occlusion
• Experimental Method: Causal mediation protocols, permutation and analytic nulls, grouped / leave-one-group-out cross-validation, paired significance testing with multiple-comparison correction, bootstrap over the correct unit of independence
• ML & LLM Tooling: PyTorch, nnsight, scikit-learn, LangChain, LangGraph, RAG, NL2SQL, supervised fine-tuning
• Programming Languages: Python, Java, TypeScript (proficient); Go, C, Scala, OCaml, Erlang (familiar)
• Web, Data & Cloud: React, SQL / PL-SQL, Supabase (PostgreSQL + RBAC), Netlify Functions, CI/CD
• Languages: Italian (native), Arabic (native), English (fluent, working language)

Projects

RecupGPT — Intelligent Natural-Language Querying
Retrieval-Augmented Generation over a food-surplus database

• Built with LangChain and LangGraph, integrating NL2SQL translation with query rewriting and routing
• Natural-language access to a Supabase database with thousands of records
• Cloud-native deployment (React, Supabase, Netlify Functions)

Extended Ricart–Agrawala — Distributed Mutual Exclusion
Distributed and Pervasive Systems (30 cum laude)

• Implemented and extended the Ricart–Agrawala algorithm, resolving correctness issues arising from removing the Lamport-clock assumptions
• Developed test-first (TDD); code under a 1-year embargo

Non-Profit Information System (B.Sc. Thesis Project)
Full-stack web application for NGO operations

• React + TypeScript frontend and Netlify Functions backend
• Real-time data synchronization using Supabase
• Custom multi-role access control (RBAC)
• Features: donation tracking, volunteer management, reporting dashboards
• In use by volunteers and coordinators in the markets of Milan and Rome