FlashAttention Authors and Contributions | Generated by AI
FlashAttention Authors Introduction
The seminal 2022 paper FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness revolutionized transformer efficiency by optimizing attention mechanisms for GPU memory and speed. It was co-authored by five researchers: Tri Dao (lead), Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Below is a brief introduction to each, focusing on their academic and professional contributions relevant to machine learning and systems.
Tri Dao
Tri Dao is an Assistant Professor of Computer Science at Princeton University, where he focuses on efficient machine learning systems and large language models. He is also the Co-founder and Chief Scientist at Together AI, a startup advancing open-source AI infrastructure. Dao earned his PhD in Computer Science from Stanford University in 2023, with prior work on scalable attention mechanisms that directly led to FlashAttention.
Daniel Y. Fu
Daniel Y. Fu is a machine learning researcher specializing in efficient architectures for large-scale models. He completed his PhD in Computer Science at Stanford University around 2024–2025, co-advised by experts in AI systems. Fu now works as a researcher at Together AI, contributing to practical implementations of high-performance transformers, building on his role in developing FlashAttention’s core algorithms.
Stefano Ermon
Stefano Ermon is an Associate Professor of Computer Science at Stanford University, affiliated with the Stanford AI Laboratory. His research bridges machine learning, probabilistic reasoning, and optimization, with applications in generative models and decision-making under uncertainty. Ermon, who joined Stanford’s faculty in 2016, has influenced scalable AI techniques, including contributions to attention efficiency in FlashAttention.
Atri Rudra
Atri Rudra is a Professor of Computer Science and Engineering at the University at Buffalo (SUNY), where he holds the Katherine Johnson Chair in Artificial Intelligence. A leading expert in data compression, streaming algorithms, and theoretical foundations of machine learning, Rudra earned his PhD from the University of Washington in 2007 and B.Tech from IIT Kharagpur. His work on IO-aware data processing was pivotal to FlashAttention’s memory optimizations.
Christopher Ré
Christopher Ré is a Professor of Computer Science at Stanford University and a prominent figure in data systems and AI. A 2015 MacArthur Fellow, Ré’s research democratizes large-scale analytics through innovations in probabilistic databases and weak supervision. With a PhD from Stanford (2009), he has shaped tools like Snorkel and influenced efficient ML pipelines, providing the systems expertise underpinning FlashAttention.
These collaborators span academia and industry, combining theory, hardware awareness, and practical AI deployment.
References
- FlashAttention Paper (arXiv)
- Tri Dao Princeton Profile
- Daniel Y. Fu LinkedIn
- Stefano Ermon Stanford Profile
- Atri Rudra UB Profile
- Christopher Ré Wikipedia
- Christopher Ré Stanford Profile