Neel Nanda
Leads the mechanistic interpretability team at Google DeepMind.
At a glance
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- Neel Nanda runs the mechanistic interpretability team at Google DeepMind, which tries to reverse-engineer the algorithms trained neural networks learn.
- Before that he did independent interpretability research and worked at Anthropic as a language model interpretability researcher under Chris Olah.
- He studied pure maths at Cambridge, graduating in 2020.
- He mentors researchers through a MATS stream and blogs at neelnanda.io.
Key facts
- Google DeepMind
- Runs the mechanistic interpretability team (as of November 2026)1
- Before
- Language model interpretability researcher at Anthropic under Chris Olah; independent interpretability research1
- Education
- Pure maths undergraduate, Cambridge, graduated 20201
- Early work
- Quant finance internships at Jane Street and Jump Trading; internships at the Future of Humanity Institute, DeepMind and the Centre for Human-Compatible AI1
- Mentoring
- Supervises researchers through his MATS stream, twice a year1
Roles
Part of
- Google DeepMindOrganization
runs the mechanistic interpretability team (as of November 2026) source on Google DeepMind
Links
Sources
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