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Samuel R. Bowman

Technical AI safety researcher, Anthropic

  • Technical AI safety researcher, Anthropic1
  • Associate Professor of Data Science and Computer Science (on long-term leave); led the Alignment Research Group 2022–24, New York University1

Samuel R. Bowman works on technical AI safety at Anthropic and is on long-term leave from New York University, where Bowman is an associate professor of data science and computer science. Bowman is first author of the SNLI natural language inference corpus and a co-author of the GLUE and SuperGLUE benchmarks, and is the final listed author of Anthropic's Petri alignment auditing tool releases.123456

At a glance

Current roles
Technical AI safety researcher +1 more
Companies
Anthropic, NYU
Sector
Company
Education
NYU, Stanford
Work listed
3
Sources cited
7
Profile reviewed

Career

Bowman earned a PhD in 2016 at the Stanford NLP Group and Stanford Linguistics, supervised by Chris Potts and Chris Manning, for work on early neural network models for text. At New York University, Bowman became an associate professor of data science and computer science, was part of the ML² Group and the CILVR Lab, and led the Alignment Research Group during 2022–24.1

Bowman now works on technical AI safety at Anthropic and is on long-term leave from NYU.1

Research

Bowman's early work includes the SNLI corpus for natural language inference (2015). With Alex Wang and colleagues, Bowman co-authored the GLUE (2018) and SuperGLUE (2019) benchmarks for evaluating general-purpose language understanding. In 2022 Bowman was first author of "Measuring Progress on Scalable Oversight for Large Language Models".2347

Bowman is the final listed author of the October 2025 Petri release, an open-source framework that uses AI agents to audit model behavior, and of the January 2026 Petri 2.0 update.56

Work and contributions

  • Petri (Inspect Petri) Open-source code

    Listed as the final author of the October 2025 Petri release and the January 2026 Petri 2.0 update on Anthropic's Alignment Science Blog.56

  • SNLI corpus Open data

    First author of "A large annotated corpus for learning natural language inference" (2015), with Gabor Angeli, Christopher Potts and Christopher D. Manning.2

  • GLUE and SuperGLUE benchmarks Published research

    Co-author of the GLUE (2018) and SuperGLUE (2019) benchmarks for general-purpose language understanding.34

Timeline

  1. Posted the SNLI natural language inference corpus paper as first author.2
  2. Completed a PhD at Stanford, supervised by Chris Potts and Chris Manning.1
  3. Co-authored the GLUE benchmark for natural language understanding.3
  4. Co-authored the SuperGLUE benchmark.4
  5. Posted "Measuring Progress on Scalable Oversight for Large Language Models" as first author.7
  6. Co-authored the release of Petri, an open-source alignment auditing tool.5
  7. Co-authored the Petri 2.0 update with Kai Fronsdal and Jonathan Michala.6

Where they work

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References

7 sources · profile reviewed Oct 22, 2026 · data updated Oct 22, 2026

  1. Sam Bowman — personal siteSamuel R. Bowman · Official page · accessed Oct 22, 2026
  2. A large annotated corpus for learning natural language inference (arXiv 1508.05326)arXiv · Paper · published Aug 21, 2015 · accessed Oct 22, 2026
  3. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (arXiv 1804.07461)arXiv · Paper · published Apr 20, 2018 · accessed Oct 22, 2026
  4. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (arXiv 1905.00537)arXiv · Paper · published May 2, 2019 · accessed Oct 22, 2026
  5. Petri: An open-source auditing tool to accelerate AI safety researchAnthropic (Alignment Science Blog) · Announcement · published Oct 6, 2025 · accessed Oct 22, 2026
  6. Petri 2.0: New Scenarios, New Model Comparisons, and Improved Eval-Awareness MitigationsAnthropic (Alignment Science Blog) · Announcement · published Jan 22, 2026 · accessed Oct 22, 2026
  7. Measuring Progress on Scalable Oversight for Large Language Models (arXiv 2211.03540)arXiv · Paper · published Nov 4, 2022 · accessed Oct 22, 2026