University of Cambridge
Tom LieberumResearch Engineer · Google DeepMindResearcher↗
Researcher
Neel Nanda
Google DeepMindStaff Research Scientist
Nanda leads the mechanistic interpretability team at Google DeepMind, which tries to reverse-engineer the internal computations of neural networks to make them safer and more transparent.[1] He is also one of the field's most prolific educators, building open tools and tutorials[2] that drew many newcomers into interpretability research.
Career & education
Current roleGoogle DeepMindApr 2026-presentStaff Research Scientist
Google DeepMindApr 2026-presentPreviouslyGoogle DeepMind2023-2026Research Engineer -> Senior Research Scientist
AnthropicNov 2021-May 2022Member of Technical Staff
Google DeepMind2023-2026AnthropicNov 2021-May 2022Education
Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations22,955As of Nov 3, '26
citations+6,536Since last snapshot, Jun 15, '26
Selected contributions
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
With Yuntao Bai, Andy L. Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain and others ·Training AI assistants to be more helpful and less harmful using human feedback.
References
- Neel Nanda — MATS mentor biographymatsprogram.org
Current program biography explicitly confirms mechanistic-interpretability team leadership and model-internals research for safety.
- Neel Nanda — personal biography and educational resourcesneelnanda.io
Lists TransformerLens, interpretability glossary, video walkthroughs and mentoring.
Based on public sources. If accuracy is important, verify details using the links or references.
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