3 sources
A useful profile for the core DeepMind contributor layer behind Chinchilla, Gopher, and Gemini rather than only the more public faces of those systems.
Researcher Profile
Editor reviewedSimon Osindero
Compute-optimal scaling for LLM training
Google DeepMind researcher spanning generative modeling and frontier multimodal systems
Important because his work spans several major eras of modern deep learning, from early generative modeling and sequence systems to the DeepMind large-model stack that culminated in Gemini.
Organizations
Google DeepMind
Labs
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last reviewed
March 18, 2026
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
WaveNet and generative sequence models
02
Large-model scaling at DeepMind
03
Gemini
04
Compute-optimal scaling for LLM training
05
Training Compute-Optimal Large Language Models
06
DeepMind
Start Here
Canonical papers, project pages, or repositories that anchor this profile.
Supporting Sources
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Related Researchers
People worth exploring next because they share topics, labs, or source material with this profile.
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A useful profile for the quieter contributor layer behind DeepMind’s frontier language-model systems, especially across Chinchilla and Gemini.
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One of the clearest people to follow for the sequence from retrieval-augmented language models to compute-optimal scaling and then into Gemini.
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Worth tracking for the DeepMind thread that links large-model scaling research to the multimodal Gemini stack, rather than treating those as separate eras.
3 sources
A useful profile for the DeepMind researchers who helped carry the lab’s language-model program from scaling-law work into Gemini rather than appearing only on the final product layer.
3 sources
A useful page for the DeepMind work that connected large-language-model scaling to the multimodal Gemini push, with a clearer safety-and-evaluation flavor than many purely scaling-focused pages.