Research Scientist at Massachusetts Institute of Technology
United States
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Summary
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Fabian Zaiser is a research scientist in computer science at MIT with a strong math background and six years of experience bridging programming languages, verification, and probabilistic programming. He specializes in probabilistic programming languages for Bayesian modeling and has formalized correctness proofs and probabilistic termination abstractions in verification-aware languages like Dafny. Fabian has practical systems experience from roles at AWS and Google, contributing to the Kani Rust verifier (including async/await support and demangled C output) and building static analyzers and developer tools. He is an experienced educator who has taught courses from analysis to algorithmics and led tutorials and admissions interviews at Oxford and Merton College. An active open-source Rust advocate, he contributes to compiler and verifier tooling, combining deep theory with hands-on implementation. Colleagues value that he moves smoothly between pen-and-paper proofs and production-quality engineering to make probabilistic correctness both rigorous and usable.
6 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Oxford
Graduate Exchange Program Mathematics and Computer Science, Graduate Exchange Program Mathematics and Computer Science at University of Toronto
Master’s Degree Mathematics, Master’s Degree Mathematics at The University of Bonn
Contributions:190 reviews, 26 commits, 46 PRs in 3 months
Contributions summary:Fabian primarily contributed to the Kani Rust Verifier by implementing features related to demangling symbol names in the GotoC code generation process. They introduced functionality to generate demangled C code, enhancing the readability of the generated output and adding tests to verify this feature. Additionally, the user worked on refactoring and improving code generation and testing infrastructure, including fixing compiletest output and adding support for generators and async/.await features.
GuBPI – an analyzer for probabilistic programs to compute guaranteed bounds on the posterior
Contributions:8 reviews, 12 commits, 7 PRs in 11 months
posteriorboundsmachine-learningprogramscompute
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Fabian Zaiser - Research Scientist at Massachusetts Institute of Technology