Ludwig Schmidt is an assistant professor and machine learning researcher with 16 years of experience making ML systems more reliable, currently splitting roles between Stanford and Anthropic in the Bay Area. He holds a PhD from MIT and has moved between top research institutions and industry labs—MIT, Berkeley, AI2, Toyota Research Institute—bringing deep academic rigor to production-focused ML problems. His background spans core numerical and systems work (notably contributions to FALCONN’s linear-algebra integration) as well as safety and robustness research for large models. Colleagues know him for translating theoretical insights into engineering improvements that tighten dependencies and reduce fragility in codebases.
16 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Massachusetts Institute of Technology
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of Cambridge
FAst Lookups of Cosine and Other Nearest Neighbors (based on fast locality-sensitive hashing)
Role in this project:
Backend Developer
Contributions:1 release, 114 commits, 13 PRs in 1 year 10 months
Contributions summary:Ludwig's contributions primarily involve modifications to the Eigen library, suggesting a focus on numerical computations and linear algebra. The commits indicate the user integrated external libraries (Eigen, Googletest, FFHT) and performed internal refactoring, specifically removing a vector access operator and adjusting paths. The user's work appears to have had the impact of improving the project's dependencies and overall codebase structure.
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Ludwig Schmidt - Assistant Professor at Stanford University