Researcher Profile

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Noam Shazeer

Transformers, Mixture-of-Experts, scaling

Researcher at Google DeepMind

One of the most important architecture-level thinkers in modern AI, with influence spanning Transformers, efficient scaling, and mixture-of-experts systems.

Organizations

Google DeepMind

Topics

Multimodal Systems & Infrastructure

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

Best First Clicks

Attention Is All You Needpaper Switch Transformerspaper GLaM: Efficient Scaling of Language Models with Mixture-of-Expertspaper

Known For

The ideas, systems, and research directions that make this person worth knowing.

Transformer-era architecture work

Mixture-of-experts scaling

Core ideas behind efficient frontier systems

Transformers, Mixture-of-Experts, scaling

Attention Is All You Need

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Start Here

Canonical papers, project pages, or repositories that anchor this profile.

Attention Is All You Needpaper Switch Transformerspaper GLaM: Efficient Scaling of Language Models with Mixture-of-Expertspaper

Related Researchers

People worth exploring next because they share topics, labs, or source material with this profile.

Shared canonical source

Ashish Vaswani

Transformers

3 sources

A foundational figure in modern sequence modeling whose work on the Transformer changed the technical direction of language and multimodal systems.

Multimodal Systems & Infrastructure

Start HereAttention Is All You Need

Shared canonical source

Barret Zoph

Trillion-parameter scaling with sparsity (Switch Transformers)

1 source

Co-authored Switch Transformers: a core reference for practical MoE scaling.

Systems & Infrastructure

Start HereSwitch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Shared canonical source

William Fedus

Trillion-parameter scaling with sparsity (Switch Transformers)

1 source

Co-authored Switch Transformers: a core reference for practical MoE scaling.

Systems & Infrastructure

Start HereSwitch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Shared canonical source

Claire Cui

Efficient MoE scaling (GLaM)

1 source

Co-authored GLaM: an influential MoE scaling reference in large language modeling.

Systems & Infrastructure

Start HereGLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Shared canonical source

Dmitry Lepikhin

Efficient MoE scaling (GLaM)

1 source

Co-authored GLaM: an influential MoE scaling reference in large language modeling.

Systems & Infrastructure

Start HereGLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Shared canonical source

Kathleen Meier-Hellstern

Efficient MoE scaling (GLaM)

1 source

Co-authored GLaM: an influential MoE scaling reference in large language modeling.

Systems & Infrastructure

Start HereGLaM: Efficient Scaling of Language Models with Mixture-of-Experts