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Dan Hendrycks - GitHub Profile (ML Safety Researcher)

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Credibility Rating

3/5
Good(3)

Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.

Rating inherited from publication venue: GitHub

Dan Hendrycks is one of the most prolific researchers at the intersection of ML robustness and AI safety; his GitHub hosts datasets and benchmarks widely used to evaluate model safety and reliability across the field.

Metadata

Importance: 72/100homepage

Summary

GitHub profile of Dan Hendrycks, a prominent ML safety researcher and director of the Center for AI Safety. His public repositories include foundational benchmarks and datasets for evaluating robustness, out-of-distribution detection, and AI safety, including MMLU, ARC, and corruption robustness benchmarks.

Key Points

  • Home to influential benchmarks like MMLU (Massive Multitask Language Understanding) used widely to evaluate LLM capabilities
  • Includes robustness benchmarks (ImageNet-C, CIFAR corruption) for measuring model reliability under distribution shift
  • Repos cover anomaly detection, calibration, and out-of-distribution detection — core technical safety concerns
  • Work directly informs AI safety evaluation methodology and is widely cited in alignment and capabilities research
  • Hendrycks is director of the Center for AI Safety (CAIS), making this profile a key resource hub for safety tooling

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# Dan Hendrycks    hendrycks

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PhD student at UC Berkeley.

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- Berkeley, California
- [danhendrycks.com](http://danhendrycks.com/)
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## [Achievements](https://github.com/hendrycks?tab=achievements)

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## [Achievements](https://github.com/hendrycks?tab=achievements)

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