--- title: "Tri Dao · Princeton University — author profile and papers" description: "Tri Dao, Princeton University: 3 papers on Elamur Research (2023–2024), h-index 16 — affiliations, topics, co-authors and who cites them in…" canonical: "https://research.elamur.ai/free/en/authors/tri-dao/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/tri-dao/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Tri Dao # Tri Dao Tri Dao has 3 papers in our library, in 2023–2024, most of them on [GPU & parallel computing](https://md.research.elamur.ai/free/en/topics/gpu-parallel-computing/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Deming Chen](https://md.research.elamur.ai/free/en/authors/deming-chen/), [Tianle Cai](https://md.research.elamur.ai/free/en/authors/tianle-cai/), [Ying Zhang](https://md.research.elamur.ai/free/en/authors/ying-zhang/). 3 papers in our library · 2023–2024 [OpenAlex](https://openalex.org/A5091734792) · [**h-index 16**](https://openalex.org/authors/A5091734792) · [59 works](https://openalex.org/works?filter=author.id:A5091734792) · [2,377 citations](https://openalex.org/works?filter=author.id:A5091734792&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/tri-dao/#papers) · [🔗 Cited 5,065 times in our library](https://md.research.elamur.ai/free/en/authors/tri-dao/cited-by/) · [17 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #1530](https://md.research.elamur.ai/free/en/paper-stars/page/16/#star-1530) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Tri+Dao) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Tri+Dao) · [dblp](https://dblp.org/search?q=Tri+Dao) · [ACL Anthology](https://aclanthology.org/search/?q=Tri+Dao) ## Affiliations [🎓**Princeton University** Princeton · United States 2023–2026 · 23 papers*current*](https://md.research.elamur.ai/free/en/universities/princeton-university/) · [🤝**Together** Lacey · United States 2025–2026 · 2 papers*current*](https://ror.org/01azqfh67) · [🎓**Stanford University** Stanford · United States 2017–2023 · 27 papers](https://md.research.elamur.ai/free/en/universities/stanford-university/) · [🎓**University at Buffalo, State University of New York** Buffalo · United States 2018–2021 · 4 papers](https://md.research.elamur.ai/free/en/universities/university-at-buffalo-state-university-of-new-york/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Hardware and Architecture · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/) 🧭 Topics 2 Research subjects and languages the work is about.[GPU & parallel computing · 2 works](https://md.research.elamur.ai/free/en/topics/gpu-parallel-computing/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2024** 2 papers](https://md.research.elamur.ai/free/en/authors/tri-dao/year/2024/) · [**2023** 1 paper](https://md.research.elamur.ai/free/en/authors/tri-dao/year/2023/) ## Papers The most cited first (OpenAlex and our library). 1. **[FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://md.research.elamur.ai/free/en/papers/dao-2023-flashattention-2-faster-attention-better/)** *(2023 · 1,240 citations)* The time and memory required by the attention layer in a Transformer grow quadratically with sequence length. 2. **[FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision](https://md.research.elamur.ai/free/en/papers/shah-2024-flashattention-3-fast-accurate-attention/)** *(2024 · 400 citations)* The attention mechanism is the main computational bottleneck of the Transformer architecture: its cost grows quadratically with sequence length. 3. **[Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://md.research.elamur.ai/free/en/papers/cai-2024-medusa-simple-llm-inference-acceleration/)** *(2024 · 278 citations)* Generating text with an LLM is slow because tokens come out one at a time. Each step has to read all of the model's weights from HBM, so the accelerator spends most of its time waiting on memory rather than computing. Speculative decoding helps, but it needs a separate draft model, which is costly to obtain and… ## All works 20 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them. 1. **[FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness ↗](https://arxiv.org/abs/2205.14135) arXiv:2205.14135 · no summary yet**Tri Dao et al.*2022 · 2,495 citations* 2. **[Mamba: Linear-Time Sequence Modeling with Selective State Spaces ↗](https://arxiv.org/abs/2312.00752) arXiv:2312.00752 · no summary yet**Albert Gu, Tri Dao*2023 · 1,726 citations* 3. [**FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning arXiv:2307.08691**Tri Dao*2023 · 1,240 citations*](https://md.research.elamur.ai/free/en/papers/dao-2023-flashattention-2-faster-attention-better/) 4. **[StarCoder: may the source be with you! ↗](https://arxiv.org/abs/2305.06161) arXiv:2305.06161 · no summary yet**Raymond Li et al.*2023 · 732 citations* 5. **[FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision](https://md.research.elamur.ai/free/en/papers/shah-2024-flashattention-3-fast-accurate-attention/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jay Shah, Ganesh Bikshandi, Ying Zhang, et al.*2024 · 400 citations* 6. [**Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads arXiv:2401.10774**Tianle Cai, Yuhong Li, Zhengyang Geng, et al.*2024 · 278 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-medusa-simple-llm-inference-acceleration/) 7. **[HiPPO: Recurrent Memory with Optimal Polynomial Projections ↗](https://arxiv.org/abs/2008.07669) arXiv:2008.07669 · no summary yet**Albert Gu et al.*2020 · 205 citations* 8. **[Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time ↗](https://arxiv.org/abs/2310.17157) ICML 2023 · no summary yet**Zichang Liu et al.*2023 · 185 citations* 9. **[Hyena Hierarchy: Towards Larger Convolutional Language Models ↗](https://arxiv.org/abs/2302.10866) arXiv:2302.10866 · no summary yet**Michael Poli et al.*2023 · 182 citations* 10. **[An Empirical Study of Mamba-based Language Models ↗](https://arxiv.org/abs/2406.07887) arXiv:2406.07887 · no summary yet**Roger Waleffe et al.*2024 · 67 citations* 11. **[The Mamba in the Llama: Distilling and Accelerating Hybrid Models ↗](https://arxiv.org/abs/2408.15237) arXiv:2408.15237 · no summary yet**Junxiong Wang; Daniele Paliotta; Avner May; Alexander M. Rush;, Tri Dao*2024 · 59 citations* 12. **[Decentralized Training of Foundation Models in Heterogeneous Environments ↗](https://arxiv.org/abs/2206.01288) arXiv:2206.01288 · no summary yet**Binhang Yuan et al.*2022 · 59 citations* 13. **[SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations ↗](https://arxiv.org/abs/2512.14080) arXiv:2512.14080 · no summary yet**Wentao Guo et al.*2025 · 14 citations* 14. **[Adaptive Hashing for Model Counting. ↗](https://openalex.org/W2966479870) Uncertainty in Artificial Intelligence · no summary yet**Jonathan Kuck et al.*2019 · 7 citations* 15. **[Beat the long tail: Distribution-Aware Speculative Decoding for RL Training ↗](https://arxiv.org/abs/2511.13841) arXiv (Cornell University) · no summary yet**Shao, Zelei et al.*2025 · 6 citations* 16. **[Transform Once: Efficient Operator Learning in Frequency Domain ↗](https://doi.org/10.52202/068431-0577) neural information processing systems · no summary yet**Michael Poli et al.*2022 · 5 citations* 17. **[Approximating the Permanent by Sampling from Adaptive Partitions ↗](https://arxiv.org/abs/1911.11856) arXiv (Cornell University) · no summary yet**Jonathan Kuck et al.*2019 · 5 citations* 18. **[ButterflyFlow: Building Invertible Layers with Butterfly Matrices ↗](https://arxiv.org/abs/2209.13774) arXiv (Cornell University) · no summary yet**Chenlin Meng et al.*2022 · 2 citations* 19. **[AI+HW 2035: Shaping the Next Decade ↗](https://arxiv.org/abs/2603.05225) arXiv (Cornell University) · no summary yet**Deming Chen et al.*2026 · 1 citation* 20. **[When RL Meets Adaptive Speculative Training: A Unified Training-Serving System ↗](https://arxiv.org/abs/2602.06932) arXiv (Cornell University) · no summary yet**Junxiong Wang et al.*2026* ## Co-authors here 4 co-authors with a page, the most papers together first. - [Deming Chen](https://md.research.elamur.ai/free/en/authors/deming-chen/) - [Tianle Cai](https://md.research.elamur.ai/free/en/authors/tianle-cai/) - [Ying Zhang](https://md.research.elamur.ai/free/en/authors/ying-zhang/) - [Yuhong Li](https://md.research.elamur.ai/free/en/authors/yuhong-li/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/) ×2: [NeurIPS 2024](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2024/) - ICML 2023 - Uncertainty in Artificial Intelligence - neural information processing systems ## Cited by Papers of our library cite their works 5,065 times (of the 149,439 papers whose bibliographies we read); [462 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/tri-dao/cited-by/). Updated 30 October 2026