World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
75
Citations
29663
World Ranking
1385
National Ranking
721

Stefano Ermon publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Stefano Ermon sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 338 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Stefano Ermon D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Stefano Ermon sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 75 D-Index — 90th percentile

90% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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