Stefano Ermon
· Probabilistic Reasoning, Machine Learning & SustainabilityStanford University · Learning, Design, and Technology
Active 2009–2025
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About
Stefano Ermon leads research that focuses on innovative computational approaches to address societal and environmental challenges of the 21st century. His work combines foundational research in artificial intelligence and machine learning with practical applications in science and engineering. The goal of his research is to enable computers to act intelligently and adaptively in increasingly complex and uncertain real-world environments.
Research topics
- Computer Science
- Artificial Intelligence
- Machine Learning
- Data science
- Engineering
- Algorithm
- Mathematics
- Remote sensing
- Applied mathematics
- Geography
Selected publications
On the Opportunities and Risks of Foundation Models
arXiv (Cornell University) · 2021 · 2169 citations
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(…
Score-Based Generative Modeling through Stochastic Differential Equations
arXiv (Cornell University) · 2020 · 1270 citations
Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data…
Closed-loop optimization of fast-charging protocols for batteries with machine learning
Nature · 2020 · 1022 citations
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
arXiv (Cornell University) · 2022 · 548 citations
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyon…
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
arXiv (Cornell University) · 2022 · 457 citations
Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading off model quality to reduce the compute complexity, but often do not achieve wall-clock speedup. We argue that a missing principle is making attention algorithms IO-aware -- accounting for reads and writes between levels of GPU memory. We propose FlashAttention, an IO-awa…
Recent grants
CAREER: Modeling and Inference for Large Scale Spatio-Temporal Data
NSF · $540k · 2017–2024
AitF: Collaborative Research: Efficient High-Dimensional Integration using Error-Correcting Codes
NSF · $360k · 2017–2021
Frequent coauthors
- 82 shared
David B. Lobell
- 71 shared
Marshall Burke
Stanford University
- 69 shared
Jiaming Song
Hengshui University
- 63 shared
Alexandre Drouin
- 63 shared
Gabriel Huang
- 60 shared
Chenlin Meng
Stanford University
- 44 shared
Burak Uzkent
Amazon (United States)
- 41 shared
Aditya Grover
Awards & honors
- ICML 2024 Best Paper Award
- ICLR 2022 Outstanding Paper Award
- ICLR 2021 Outstanding Paper Award
- ISSNAF Young Investigator Award
- Sloan Research Fellowship
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