Ben Sherman is a software engineer and MIT-trained researcher with 13 years of experience building statistically grounded systems for perception, prediction, and trustworthy AI. Currently at Waymo, he designs optimal allocation and probabilistic simulation techniques to reduce variance in performance estimates and to better quantify rare-event rates in autonomous driving. His PhD work produced programming languages for sound computation with continuous values and formalized semantics for automatic differentiation, blending functional programming, topology, and category theory. He has practical experience applying Bayesian and particle-filter methods to streaming inference and has contributed to the Idris dependently typed language core, improving short-circuiting and documentation in the Prelude. Based in the San Francisco Bay Area, Ben combines deep theoretical rigor with production-focused engineering to make probabilistic systems both precise and usable.
13 years of coding experience
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Massachusetts Institute of Technology
Bachelor of Science - BS, Mathematics, Bachelor of Science - BS, Mathematics at Yale University
A Dependently Typed Functional Programming Language
Role in this project:
Back-end Developer
Contributions:62 commits, 3 PRs, 4 comments in 9 months
Contributions summary:Ben made several contributions focused on improving the Idris programming language's core functionality. This includes generalizing the list eliminator to support more types and modifying core functions like `and`, `or`, `any`, and `all` to short-circuit. Additionally, the user added documentation to existing functions within the Prelude library, enhancing the usability and understanding of the language.
Contributions:115 commits, 97 pushes, 2 branches in 2 months
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