3 sources
A high-signal person to follow for the research arc from early transformer work into later sequence, vision, and multimodal model design.
Start HereInceptive
Researcher Profile
Editor reviewedNiki Parmar
Transformers and sequence modeling
A foundational transformer researcher whose work still matters because it connects the original architecture shift to later efforts on efficiency, scaling, and sequence modeling infrastructure.
Organizations
Google Research
Topics
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
The original Transformer architecture
02
Efficient sequence modeling infrastructure
03
Scaling sequence models across large compute clusters
04
Transformers and sequence modeling
05
Attention Is All You Need
06
Transformers
Start Here
Canonical papers, project pages, or repositories that anchor this profile.
Related Researchers
People worth exploring next because they share topics, labs, or source material with this profile.
3 sources
A foundational figure in modern sequence modeling whose work on the Transformer changed the technical direction of language and multimodal systems.
Start HereAttention Is All You Need
3 sources
One of the clearest people to study if you want the throughline from early neural sequence models to transformers, efficient long-context variants, and modern reasoning systems.
3 sources
One of the most important architecture-level thinkers in modern AI, with influence spanning Transformers, efficient scaling, and mixture-of-experts systems.
Start HereAttention Is All You Need
4 sources
One of the clearest people to study if you want the throughline from early neural sequence models to transformers, efficient long-context variants, and modern reasoning systems.
3 sources
A foundational transformer co-author who is now worth following for a very different reason: he is one of the few people trying to build a serious frontier lab around alternatives to the default scaling path.
Start HereAbout Sakana AI