Assistant Professor of Computer Science at Princeton University, leading the Dao AI Lab.
Co-founder & Chief Scientist of Together AI.

CV (updated 01/2026)

Previously: PhD, Department of Computer Science, Stanford University

Research Interests

Machine learning and systems, with a focus on efficient training and inference:

• Hardware-aware algorithms.
• Sequence models with long-range memory.

Current PhD Students

• Ted Zadouri
• Berlin Chen
• Wentao Guo
• Xinle Cheng (co-advised with Ravi Netravali)
• Lijie Yang (co-advised with Ravi Netravali)
• Liane Galanti (co-advised with Elad Hazan)
• Mayank Mishra (co-advised with Ion Stoica and Joey Gonzalez)
• Ze-Wei Liou

Selected Honors and Awards

• Schmidt Sciences AI2050 Fellowship, 2025.
• Google ML and Systems Junior Faculty Awards, 2025.
• Google Research Scholar, 2025.
• Conference on Machine Learning and Systems (MLSys), Best Paper Honorable Mention, 2026.
• Conference on Machine Learning and Systems (MLSys), Outstanding Paper Honorable Mention, 2025.
• Conference on Language Modeling (COLM), Outstanding Paper, 2024.
• International Conference on Machine Learning (ICML), Outstanding Paper runner-up, 2022.

1. Marconi: Prefix Caching for the Era of Hybrid LLMs

   Rui Pan, Zhuang Wang, Zhen Jia, and 5 more authors

   In Machine Learning and Systems (MLSys), 2025

   arXiv Code

   Outstanding Paper Honorable Mention

2. FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

   Jay Shah^*, Ganesh Bikshandi^*, Ying Zhang, and 3 more authors

   In Advances in Neural Information Processing Systems (NeurIPS), 2024

   arXiv Code
3. Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

   Tri Dao^* and Albert Gu^*

   In International Conference on Machine Learning (ICML), 2024

   arXiv Code
4. Mamba: Linear-Time Sequence Modeling with Selective State Spaces

   Albert Gu^* and Tri Dao^*

   Conference on Language Modeling (COLM), 2023

   arXiv Code

   Outstanding Paper

5. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

   Tri Dao, Daniel Y. Fu, Stefano Ermon, and 2 more authors

   In Advances in Neural Information Processing Systems, 2022

   arXiv Code

   Best Paper award at the ICML Hardware Aware Efficient Training Workshop 2022, Inaugural Stanford Open Source Software Prize 2024

6. Monarch: Expressive Structured Matrices for Efficient and Accurate Training

   Tri Dao, Beidi Chen, Nimit Sohoni, and 7 more authors

   In International Conference on Machine Learning (ICML), 2022

   arXiv Code

   Outstanding Paper runner-up