World's Best Scientists 2026 revealed!
Stefanie Jegelka

Stefanie Jegelka

D-Index & Metrics

Computer Science

D-Index
44
Citations
12921
World Ranking
7406
National Ranking
3230

Stefanie Jegelka 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 Stefanie Jegelka 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: 151 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Stefanie Jegelka 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 Stefanie Jegelka 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: 44 D-Index — 48th percentile

48% 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 - Fellow of Alfred P. Sloan Foundation

Overview

Stefanie Jegelka is affiliated with MIT in the United States and specializes in computer science, with a focus on artificial intelligence. Their research encompasses subfields such as computer vision and pattern recognition, molecular biology, materials chemistry, and computational theory and mathematics.

Their work extensively covers topics including advanced graph neural networks, domain adaptation and few-shot learning, neural networks and their applications, stochastic gradient optimization techniques, machine learning and algorithms, adversarial robustness in machine learning, and topic modeling.

Selected recent papers authored or co-authored by Stefanie Jegelka include:

  • Formal Semantics for Kolmogorov-Arnold Network Representations of Operational Games, 2025, Zenodo (CERN European Organization for Nuclear Research)
  • Graph neural networks, 2024, Nature Reviews Methods Primers
  • Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks, 2020, Journal of Chemical Information and Modeling
  • How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks, 2020, arXiv (Cornell University)
  • Robust Contrastive Learning against Noisy Views, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors collaborating with Stefanie Jegelka include:

  • Ching-Yao Chuang
  • Yisen Wang
  • Khashayar Gatmiry
  • Antonio Torralba
  • Joshua Robinson

Stefanie Jegelka has published primarily in venues such as arXiv (Cornell University), Nature Reviews Methods Primers, Journal of Chemical Information and Modeling, the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), and Nature Communications.

The researcher has been recognized as a Fellow of the Alfred P. Sloan Foundation in 2018.

Best Publications

  • How Powerful are Graph Neural Networks

    Keyulu Xu;Weihua Hu;Jure Leskovec;Stefanie Jegelka

  • Deep Metric Learning via Lifted Structured Feature Embedding

    Hyun Oh Song;Yu Xiang;Stefanie Jegelka;Silvio Savarese

  • Representation Learning on Graphs with Jumping Knowledge Networks

    Keyulu Xu;Chengtao Li;Yonglong Tian;Tomohiro Sonobe

  • Deep Metric Learning via Facility Location

    Hyun Oh Song;Stefanie Jegelka;Vivek Rathod;Kevin Murphy

  • Debiased Contrastive Learning

    Ching-Yao Chuang;Joshua Robinson;Yen-Chen Lin;Antonio Torralba

  • Max-value Entropy Search for Efficient Bayesian Optimization

    Zi Wang;Stefanie Jegelka

  • On learning to localize objects with minimal supervision

    Hyun Oh Song;Ross Girshick;Stefanie Jegelka;Julien Mairal

  • Submodularity beyond submodular energies: Coupling edges in graph cuts

    Stefanie Jegelka;Jeff Bilmes

  • Virtual screening of inorganic materials synthesis parameters with deep learning

    Edward Kim;Kevin Huang;Stefanie Jegelka;Elsa Olivetti

  • Weakly-supervised Discovery of Visual Pattern Configurations

    Hyun Oh Song;Yong Jae Lee;Stefanie Jegelka;Trevor Darrell

  • ResNet with one-neuron hidden layers is a Universal Approximator

    Hongzhou Lin;Stefanie Jegelka

  • How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

    Keyulu Xu;Mozhi Zhang;Jingling Li;Simon Shaolei Du

  • Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks.

    Edward Kim;Zach Jensen;Alexander van Grootel;Kevin Huang

  • Fast Semidifferential-based Submodular Function Optimization

    Rishabh Iyer;Stefanie Jegelka;Jeff Bilmes

  • Batched Large-scale Bayesian Optimization in High-dimensional Spaces

    Zi Wang;Clement Gehring;Pushmeet Kohli;Stefanie Jegelka

  • Generalization and Representational Limits of Graph Neural Networks

    Vikas K Garg;Stefanie Jegelka;Tommi Jaakkola

  • What Can Neural Networks Reason About

    Keyulu Xu;Jingling Li;Mozhi Zhang;Simon S. Du

  • Contrastive Learning with Hard Negative Samples

    Joshua David Robinson;Ching-Yao Chuang;Suvrit Sra;Stefanie Jegelka

  • Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions

    Rishabh K Iyer;Stefanie Jegelka;Jeff A Bilmes

  • A Principled Deep Random Field Model for Image Segmentation

    Pushmeet Kohli;Anton Osokin;Stefanie Jegelka

  • Adversarially Robust Optimization with Gaussian Processes

    Ilija Bogunovic;Jonathan Scarlett;Stefanie Jegelka;Volkan Cevher

  • Distributionally Robust Optimization and Generalization in Kernel Methods

    Matthew Staib;Stefanie Jegelka

Frequent Co-Authors

Jeff A. Bilmes
Jeff A. Bilmes University of Washington
Andreas Krause
Andreas Krause ETH Zurich
Ken-ichi Kawarabayashi
Ken-ichi Kawarabayashi National Institute of Informatics
Trevor Darrell
Trevor Darrell University of California, Berkeley
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Arthur Gretton
Arthur Gretton University College London
Jiashi Feng
Jiashi Feng ByteDance

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