Principal Applied Scientist at Amazon Web Services (AWS)
Cupertino, California, United States
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Summary
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Rockstar
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Soonho Kong is a Principal Applied Scientist with 13 years of experience building systems that bridge large-scale AI and formal verification, currently leading AWS's AILean effort to combine LLM creativity with Lean4 rigor. He holds a PhD in Computer Science from Carnegie Mellon and has a track record in applied research roles at Toyota Research Institute and AWS focused on provable security and trustworthy AI. An active open-source contributor, he has improved foundational projects like the Lean theorem prover and added Lean language support to the popular Ace editor, showing depth in both core systems and developer tooling. Known for tackling hard integration problems—build systems, dependency management, and language-mode tooling—he brings a rare mix of theorem-proving expertise and production-grade engineering from Cupertino.
13 years of coding experience
8 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Seoul National University
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Carnegie Mellon University
Contributions:829 commits, 16 PRs, 107 pushes in 2 years 11 months
Contributions summary:Soonho contributed to the Lean Theorem Prover by adding and modifying CMake files for the GMP and Tcmalloc libraries, indicating a focus on build system configuration and dependency management. Their work involved fixing friend issues in the mpq/mpz library and implementing pretty-print functionality. These changes suggest a role focused on improving the core library and build process of the theorem prover.
Contributions:9 commits, 1 PR, 11 comments in 1 day
Contributions summary:Soonho primarily contributed to the development of a Lean mode for the Ace editor. Their work included implementing syntax highlighting rules and creating a demo file to showcase the Lean language support. They also made improvements by cleaning up and optimizing the code, including the use of `defaultToken` for more efficient processing.
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