Percy Liang: Research Profile and Initiatives for Open AI Development

LinkPercy LiangResource

The academic homepage of Stanford computer science professor Percy Liang outlines his core research directions, teaching portfolio, and academic initiatives. Liang focuses on foundational questions in machine learning, radically open model creation through the Marin project, and reproducible research via CodaLab Worksheets.

Key points

  1. Percy Liang investigates core questions in learning and intelligence, including data-limited learning in infinite-compute regimes and modelling curiosity.
  2. Through the Marin project, he advances 'open development', preregistering both successful and failed experiments in public view beyond conventional open-source and open-weight models.
  3. His teaching includes CS336 ('Language Models from Scratch'), aiming to democratise the understanding and construction of foundation models.
  4. Liang created CodaLab Worksheets to track the full provenance of research experiments from raw data to executable papers.
  5. The page tracks a prominent network of current students, postdocs, and alumni now working across major AI labs and academic institutions.

Why it matters

As frontier model development increasingly centralises inside commercial labs, Liang advocates for open development that exposes internal experimentation workflows. This degree of transparency provides the wider scientific community with verifiable insights into the actual engineering and failure modes of foundation models.

Analysis

Liang's work bridges theoretical machine learning and practical research tooling. Initiatives like Marin and the CS336 curriculum address the transparency gap in generative AI by documenting the messy, iterative reality of model training. This methodological openness challenges standard academic publication bias and corporate secrecy.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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