World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
75
Citations
29663
World Ranking
1385
National Ranking
720

Research.com Recognitions

  • 2018 - Hellman Fellow

Overview

Stefano Ermon is affiliated with Stanford University in the United States and works primarily in the field of Computer Science. Their research covers various subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Statistical and Nonlinear Physics, and Electrical and Electronic Engineering.

Their research topics reflect a focus on generative and adaptive machine learning methodologies. Notable areas include:

  • Generative Adversarial Networks and Image Synthesis
  • Domain Adaptation and Few-Shot Learning
  • Topic Modeling
  • Model Reduction and Neural Networks
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning

Stefano Ermon has contributed to several recent papers with significant citations in reputable venues. Selected examples include:

  • "On the Opportunities and Risks of Foundation Models" (2021, arXiv (Cornell University))
  • "Score-Based Generative Modeling through Stochastic Differential Equations" (2020, arXiv (Cornell University))
  • "Closed-loop optimization of fast-charging protocols for batteries with machine learning" (2020, Nature)
  • "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness" (2022, arXiv (Cornell University))
  • "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa" (2020, Nature Communications)

Frequent collaborators of Stefano Ermon include:

  • Yoshua Bengio
  • Jeremy Irvin
  • David B. Lobell
  • Alexandre Lacoste
  • Pau Rodríguez

The venues where Stefano Ermon has published extensively highlight ties to preeminent research platforms and conferences. These venues include:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Science
  • Remote Sensing of Environment

Stefano Ermon has also contributed to book publications, including a title published by Washington, DC: World Bank eBooks, namely "Dynamic, High-Resolution Wealth Measurement in Data-Scarce Environments" scheduled for 2025.

Among awards received, Stefano Ermon was recognized as a Hellman Fellow in 2018.

Best Publications

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Generative Adversarial Imitation Learning

    Jonathan Ho;Stefano Ermon

  • Combining satellite imagery and machine learning to predict poverty

    Neal Jean;Marshall Burke;Marshall Burke;Michael Xie;W. Matthew Davis

  • Score-Based Generative Modeling through Stochastic Differential Equations

    Yang Song;Jascha Sohl-Dickstein;Diederik P Kingma;Abhishek Kumar

  • Denoising Diffusion Implicit Models

    Jiaming Song;Chenlin Meng;Stefano Ermon

  • Generative Modeling by Estimating Gradients of the Data Distribution

    Yang Song;Stefano Ermon

  • Closed-loop optimization of fast-charging protocols for batteries with machine learning.

    Peter M. Attia;Aditya Grover;Norman Jin;Kristen A. Severson

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

    Unknown

  • Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.

    Chi-Sing Ho;Neal Jean;Catherine A. Hogan;Lena Blackmon

  • PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

    Yang Song;Taesup Kim;Sebastian Nowozin;Stefano Ermon

  • A Survey on Behavior Recognition Using WiFi Channel State Information

    Siamak Yousefi;Hirokazu Narui;Sankalp Dayal;Stefano Ermon

  • Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data

    Jiaxuan You;Xiaocheng Li;Melvin Low;David B. Lobell

  • InfoVAE: Information Maximizing Variational Autoencoders

    Shengjia Zhao;Jiaming Song;Stefano Ermon

  • Transfer learning from deep features for remote sensing and poverty mapping

    Michael Xie;Neal Jean;Marshall Burke;David Lobell

  • A DIRT-T Approach to Unsupervised Domain Adaptation

    Rui Shu;Hung H. Bui;Hirokazu Narui;Stefano Ermon

  • Direct Preference Optimization: Your Language Model is Secretly a Reward Model

    Unknown

  • Improved Techniques for Training Score-Based Generative Models

    Yang Song;Stefano Ermon

  • Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

    Christopher Yeh;Anthony Perez;Anne Driscoll;George Azzari

  • Accurate Uncertainties for Deep Learning Using Calibrated Regression.

    Volodymyr Kuleshov;Nathan Fenner;Stefano Ermon

  • Using satellite imagery to understand and promote sustainable development

    Marshall Burke;Marshall Burke;Anne Driscoll;David B. Lobell;Stefano Ermon

  • MOPO: Model-based Offline Policy Optimization

    Tianhe Yu;Garrett Thomas;Lantao Yu;Stefano Ermon

  • InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

    Yunzhu Li;Jiaming Song;Stefano Ermon

  • HiPPO: Recurrent Memory with Optimal Polynomial Projections

    Albert Gu;Tri Dao;Stefano Ermon;Atri Rudra

  • Sliced Score Matching: A Scalable Approach to Density and Score Estimation

    Yang Song;Sahaj Garg;Jiaxin Shi;Stefano Ermon

Frequent Co-Authors

David B. Lobell
David B. Lobell Stanford University
Marshall Burke
Marshall Burke Stanford University
Carla P. Gomes
Carla P. Gomes Cornell University
Bart Selman
Bart Selman Cornell University
Ashish Sabharwal
Ashish Sabharwal Allen Institute for Artificial Intelligence
William C. Chueh
William C. Chueh Stanford University
Noah D. Goodman
Noah D. Goodman Stanford University
Dorsa Sadigh
Dorsa Sadigh Stanford University
Tengyu Ma
Tengyu Ma Stanford University
Stephen J. Harris
Stephen J. Harris Lawrence Berkeley National Laboratory

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