World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
4756
World Ranking
13114
National Ranking
5272

Gregory Valiant 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 Gregory Valiant 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: 142 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Gregory Valiant 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 Gregory Valiant 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: 32 D-Index — 10th percentile

10% 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

  • 2017 - Hellman Fellow
  • 2016 - Fellow of Alfred P. Sloan Foundation

Overview

Gregory Valiant is affiliated with Stanford University in the United States and specializes primarily in the field of Computer Science. Their research contributions span multiple subfields, including Artificial Intelligence, Molecular Biology, Management Science and Operations Research, Computational Theory and Mathematics, and Computer Networks and Communications.

The scientist's work covers a variety of key topics such as Machine Learning and Algorithms, Advanced Bandit Algorithms Research, Domain Adaptation and Few-Shot Learning, Machine Learning and Data Classification, Natural Language Processing Techniques, Complexity and Algorithms in Graphs, and Stochastic Gradient Optimization Techniques.

Valiant's publication record includes papers appearing in venues like arXiv (Cornell University), Leibniz-Zentrum für Informatik (Schloss Dagstuhl), eLife, bioRxiv (Cold Spring Harbor Laboratory), and Cambridge University Press eBooks. The most frequent venue for their papers is arXiv, with 21 publications.

Recent papers authored or co-authored by Valiant include:

  • What Can Transformers Learn In-Context? A Case Study of Simple Function Classes, 2022, arXiv (Cornell University)
  • On the Generalization Effects of Linear Transformations in Data Augmentation, 2020, arXiv (Cornell University)
  • ReporterSeq reveals genome-wide dynamic modulators of the heat shock response across diverse stressors, 2021, eLife
  • Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training, 2021, arXiv (Cornell University)

Frequent collaborators in their research include Vatsal Sharan, Shivam Garg, Annie Marsden, Mingda Qiao, and Percy Liang.

Gregory Valiant has been recognized with several awards, notably the Hellman Fellow award in 2017 and being named a Fellow of the Alfred P. Sloan Foundation in 2016.

Best Publications

  • Settling the Polynomial Learnability of Mixtures of Gaussians

    Ankur Moitra;Gregory Valiant

  • An Automatic Inequality Prover and Instance Optimal Identity Testing

    Gregory Valiant;Paul Valiant

  • Estimating the unseen: an n/log(n)-sample estimator for entropy and support size, shown optimal via new CLTs

    Gregory Valiant;Paul Valiant

  • Learning from untrusted data

    Moses Charikar;Jacob Steinhardt;Gregory Valiant

  • Efficiently learning mixtures of two Gaussians

    Adam Tauman Kalai;Ankur Moitra;Gregory Valiant

  • What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

    Unknown

  • Optimal algorithms for testing closeness of discrete distributions

    Siu-On Chan;Ilias Diakonikolas;Gregory Valiant;Paul Valiant

  • The Power of Linear Estimators

    Gregory Valiant;Paul Valiant

  • Learning Polynomials with Neural Networks

    Alexandr Andoni;Rina Panigrahy;Gregory Valiant;Li Zhang

  • Estimating the Unseen: Improved Estimators for Entropy and Other Properties

    Gregory Valiant;Paul Valiant

  • An Automatic Inequality Prover and Instance Optimal Identity Testing

    Gregory Valiant;Paul Valiant

  • Estimating the Unseen: Improved Estimators for Entropy and other Properties

    Paul Valiant;Gregory Valiant

  • Braess's Paradox in large random graphs

    Gregory Valiant;Tim Roughgarden

  • Designing Network Protocols for Good Equilibria

    Ho-Lin Chen;Tim Roughgarden;Gregory Valiant

  • Finding Correlations in Subquadratic Time, with Applications to Learning Parities and Juntas

    Gregory Valiant

  • A CLT and tight lower bounds for estimating entropy.

    Gregory Valiant;Paul Valiant

  • Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers

    Jacob Steinhardt;Moses Charikar;Gregory Valiant

  • Finding Correlations in Subquadratic Time, with Applications to Learning Parities and the Closest Pair Problem

    Gregory Valiant

  • Testing k-modal distributions: optimal algorithms via reductions

    Constantinos Daskalakis;Ilias Diakonikolas;Rocco A. Servedio;Gregory Valiant

  • Making AI Forget You: Data Deletion in Machine Learning

    Antonio Ginart;Melody Guan;Gregory Valiant;James Y. Zou

  • On Learning Algorithms for Nash Equilibria

    Constantinos Daskalakis;Rafael Frongillo;Christos H. Papadimitriou;George Pierrakos

  • Size and Treewidth Bounds for Conjunctive Queries

    Georg Gottlob;Stephanie Tien Lee;Gregory Valiant;Paul Valiant

  • Disentangling Gaussians

    Adam Tauman Kalai;Ankur Moitra;Gregory Valiant

  • Memory, Communication, and Statistical Queries

    Jacob Steinhardt;Gregory Valiant;Stefan Wager

  • Finding Correlations in Subquadratic Time, with Applications to Learning Parities and Juntas with Noise.

    Gregory Valiant

Frequent Co-Authors

Peter Bailis
Peter Bailis Stanford University
Sham M. Kakade
Sham M. Kakade Harvard University
Tim Roughgarden
Tim Roughgarden Columbia University
James Zou
James Zou Stanford University
Christos H. Papadimitriou
Christos H. Papadimitriou Columbia University
Moses Charikar
Moses Charikar Stanford University
Ilias Diakonikolas
Ilias Diakonikolas University of Wisconsin–Madison
Daniel G. MacArthur
Daniel G. MacArthur Garvan Institute of Medical Research

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