World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
13689
World Ranking
4507
National Ranking
2110

Tengyu Ma 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 Tengyu Ma 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: 234 publications — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Tengyu Ma 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 Tengyu Ma 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: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Tengyu Ma is affiliated with Stanford University in the United States and has contributed extensively to the field of computer science with a focus on artificial intelligence and related subfields. Their body of work spans numerous papers and collaborations across prominent venues in the domain.

Their recent publications include:

  • On the Opportunities and Risks of Foundation Models, 2021, arXiv (Cornell University)
  • Toward Fast, Flexible, and Robust Low-Light Image Enhancement, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • MOPO: Model-based Offline Policy Optimization, 2020, arXiv (Cornell University)
  • SAM 2: Segment Anything in Images and Videos, 2024, arXiv (Cornell University)

Tengyu Ma frequently collaborates with several researchers, including:

  • Percy Liang
  • Sang Michael Xie
  • Colin Wei
  • Risheng Liu
  • Ananya Kumar

The venues in which Tengyu Ma most often publishes include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • The British Journal of Psychiatry

Their research primarily falls under the broad field of computer science, with particular focus on subfields such as:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Management Science and Operations Research
  • Computational Theory and Mathematics
  • Molecular Biology

Tengyu Ma's work covers a variety of main topics, including:

  • Domain Adaptation and Few-Shot Learning
  • Topic Modeling
  • Natural Language Processing Techniques
  • Stochastic Gradient Optimization Techniques
  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Adversarial Robustness in Machine Learning

Best Publications

  • On the Opportunities and Risks of Foundation Models.

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

  • A Simple but Tough-to-Beat Baseline for Sentence Embeddings

    Sanjeev Arora;Yingyu Liang;Tengyu Ma

  • Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

    Kaidi Cao;Colin Wei;Adrien Gaidon;Nikos Arechiga

  • Generalization and Equilibrium in Generative Adversarial Nets (GANs)

    Sanjeev Arora;Rong Ge;Yingyu Liang;Tengyu Ma

  • Matrix Completion has No Spurious Local Minimum

    Rong Ge;Jason D. Lee;Tengyu Ma

  • A Latent Variable Model Approach to PMI-based Word Embeddings

    Sanjeev Arora;Yuanzhi Li;Yingyu Liang;Tengyu Ma

  • Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling

    Unknown

  • Provable Bounds for Learning Some Deep Representations

    Sanjeev Arora;Aditya Bhaskara;Rong Ge;Tengyu Ma

  • Identity Matters in Deep Learning

    Moritz Hardt;Tengyu Ma

  • MOPO: Model-based Offline Policy Optimization

    Tianhe Yu;Garrett Thomas;Lantao Yu;Stefano Ermon

  • Gradient Descent Learns Linear Dynamical Systems

    Moritz Hardt;Tengyu Ma;Benjamin Recht

  • Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis

    Unknown

  • Linear Algebraic Structure of Word Senses, with Applications to Polysemy

    Sanjeev Arora;Yuanzhi Li;Yingyu Liang;Tengyu Ma

  • Finding approximate local minima faster than gradient descent

    Naman Agarwal;Zeyuan Allen-Zhu;Brian Bullins;Elad Hazan

  • Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations

    Yuanzhi Li;Tengyu Ma;Hongyang Zhang

  • Fixup Initialization: Residual Learning Without Normalization

    Hongyi Zhang;Yann N. Dauphin;Tengyu Ma

  • Simple, Efficient, and Neural Algorithms for Sparse Coding

    Sanjeev Arora;Rong Ge;Tengyu Ma;Ankur Moitra

  • Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks

    Yuanzhi Li;Colin Wei;Tengyu Ma

  • Verified Uncertainty Calibration

    Ananya Kumar;Percy S. Liang;Tengyu Ma

  • LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross- Modal Fusion

    Unknown

  • Communication lower bounds for statistical estimation problems via a distributed data processing inequality

    Mark Braverman;Ankit Garg;Tengyu Ma;Huy L. Nguyen

  • Understanding Self-Training for Gradual Domain Adaptation

    Ananya Kumar;Tengyu Ma;Percy Liang

  • Learning one-hidden-layer neural networks with landscape design

    Rong Ge;Jason D. Lee;Tengyu Ma

Frequent Co-Authors

Sanjeev Arora
Sanjeev Arora Princeton University
Rong Ge
Rong Ge Duke University
Jason D. Lee
Jason D. Lee Princeton University
Yuanzhi Li
Yuanzhi Li Carnegie Mellon University
Yingyu Liang
Yingyu Liang University of Wisconsin–Madison
Percy Liang
Percy Liang Stanford University
Elad Hazan
Elad Hazan Princeton University
Stefano Ermon
Stefano Ermon Stanford University
Adrien Gaidon
Adrien Gaidon Stanford University
Yann N. Dauphin
Yann N. Dauphin Google (United States)

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