Santiago Zanella-béguelin is a Principal Researcher at Microsoft Research Cambridge with 13 years of experience focused on the safety, reliability, security, and privacy of AI systems, especially those built from unreliable generative components. His background spans formal verification, programming languages, and cryptography, beginning with a PhD on formally verifying cryptographic proofs and research roles at Inria and IMDEA. At Microsoft he contributes to Confidential Computing and brings practical security engineering chops, including contributions to the formally verified HACL* cryptographic library where he worked on builds, verification hints, and integrating HSalsa20. He combines deep theory and hands-on systems work, routinely bridging formal methods and production-grade cryptography to make AI and cloud systems more auditable and robust.
HACL*, a formally verified cryptographic library written in F*
Role in this project:
Back-end Developer & Security Engineer
Contributions:3 reviews, 1040 commits, 37 PRs in 3 years 5 months
Contributions summary:Santiago's commits focus on the formally verified cryptographic library HACL*. The contributions involve modifications to the build process, specifically to the Makefiles and the inclusion of hints for verification, which suggests a deep understanding of the build system and the formal verification process. The user integrated external cryptographic code (HSalsa20), indicating a familiarity with different cryptographic primitives and potentially security engineering aspects of the project. Additionally, the commits include anonymization of comments, highlighting attention to detail and potentially security-conscious practices.
Contributions:4 pushes, 3 branches in 9 years 3 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial
Santiago Zanella-béguelin - Principal Researcher at Microsoft