Matteo Boglioni
AI Researcher

Hi, I'm Matteo Boglioni

Research Intern at Mila, previously MSc at ETH Zürich. I focus on red-teaming and model auditing to uncover AI vulnerabilities and improve robustness, security, and responsible deployment.

About Me

I'm particularly interested in red-teaming and model auditing as proactive techniques to uncover existing models' vulnerabilities. I aim to assess and expose flaws and inconsistencies, both in performance and security, to increase robustness and reliability towards distribution shifts, adversarial inputs, and unintended behaviors.

I believe this step is crucial for a responsible and ethical deployment of AI.

Machine Learning Privacy & SecurityAI SafetyLLM Robustness & GeneralizationRed-teaming & Model AuditingDifferential Privacy

When I'm not doing research

Climbing
Road Cycling
Videomaking
Traveling & Backpacking

Latest Posts

Experience

Education and professional experience.

Mila — Quebec AI Institute

Research Intern

Oct 2025 – Feb 2026

Research on local precision auditing of machine unlearning methods.

ETH Zürich

MSc Computer Science — Machine Intelligence

Sep 2023 – Nov 2025

Focus on machine learning, AI safety, and robustness. Thesis on privacy auditing of DP-SGD.

Carnegie Mellon University

Visiting Research Scholar

Mar 2025 – Sep 2025

Research on Differential Privacy Auditing, under Prof. Zhiwei Steven Wu.

University of Wisconsin-Madison

ECE Visiting Student

Jan 2023 – May 2023

Exchange semester in Electrical and Computer Engineering.

Politecnico di Milano

BSc Engineering of Computing Systems

Sep 2020 – Jul 2023

Undergraduate studies in computer engineering with focus on systems and software development.

Publications

Selected research publications.

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference

Terrance Liu, Matteo Boglioni, Yiwei Fu, Shengyuan Hu, Pratiksha Thaker, Zhiwei Steven Wu

Workshop on Theory and Practice of Differential Privacy, 2025

Privacy AuditingMembership InferenceDifferential Privacy
Optimizing Canaries for Privacy Auditing with Metagradient Descent

Optimizing Canaries for Privacy Auditing with Metagradient Descent

Matteo Boglioni, Terrance Liu, Andrew Ilyas, Zhiwei Steven Wu

ICLR, 2026

Privacy AuditingDP-SGDMetagradients
Do Generalisation Results Generalise?

Do Generalisation Results Generalise?

Matteo Boglioni, Andrea Sgobbi, Gabriel Tavernini, Francesco Rita, Marius Mosbach, Tiago Pimentel

arXiv, 2025

GeneralizationNLPEvaluation

Get In Touch

Feel free to reach out for collaborations or questions.