4 sources
One of the clearest researchers to follow for efficient sequence-model systems, especially the line of work that made frontier training and inference materially faster rather than merely cleaner on paper.
Start HereTri Dao
Researcher Profile
Editor reviewedAlbert Gu
State space models for sequence modeling
Assistant professor at Carnegie Mellon University
A key researcher for understanding why state-space models became a serious alternative to standard transformer stacks rather than a recurring side path.
Organizations
Carnegie Mellon University
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
Official And External Links
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
State-space models for sequence modeling
02
Mamba
03
Long-context and efficient sequence architectures
04
State space models for sequence modeling
05
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
06
Mamba (GitHub)
Start Here
Canonical papers, project pages, or repositories that anchor this profile.
Signature Works
Additional papers, projects, or repositories that help flesh out the profile.
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