World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
87
Citations
28939
World Ranking
728
National Ranking
385

Sham M. Kakade 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 Sham M. Kakade 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: 244 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Sham M. Kakade 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 Sham M. Kakade 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: 87 D-Index — 95th percentile

95% of scientists in this discipline score the same or lower.

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

Overview

Sham M. Kakade is affiliated with Harvard University in the United States and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence.

The primary fields of study covered in their research include:

  • Computer Science

Their work spans several subfields, notably:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Computational Mechanics
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Kakade's research topics highlight areas such as:

  • Reinforcement Learning in Robotics
  • Domain Adaptation and Few-Shot Learning
  • Stochastic Gradient Optimization Techniques
  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Neural Networks and Applications
  • Sparse and Compressive Sensing Techniques

Selected recent papers include:

  • "Robust Aggregation for Federated Learning", 2022, IEEE Transactions on Signal Processing
  • "Soft Threshold Weight Reparameterization for Learnable Sparsity", 2020, arXiv (Cornell University)
  • "On Nonconvex Optimization for Machine Learning", 2021, Journal of the ACM
  • "Few-Shot Learning via Learning the Representation, Provably", 2020, arXiv (Cornell University)
  • "Optimal Regularization Can Mitigate Double Descent", 2020, arXiv (Cornell University)

The frequent publication venues where Kakade's work appears are:

  • arXiv (Cornell University)
  • IEEE Transactions on Signal Processing
  • Journal of the ACM
  • Singapore Management University Institutional Knowledge (InK) (Singapore Management University)
  • Journal of Real Estate Construction & Management

Among frequent coauthors collaborating with Kakade are:

  • Nikhil Vyas
  • Depen Morwani
  • Jason D. Lee
  • Akshay Krishnamurthy
  • Eran Malach

Best Publications

  • Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

    Niranjan Srinivas;Andreas Krause;Matthias Seeger;Sham M. Kakade

  • Tensor decompositions for learning latent variable models

    Animashree Anandkumar;Rong Ge;Daniel Hsu;Sham M. Kakade

  • A Natural Policy Gradient

    Sham M Kakade

  • Cover trees for nearest neighbor

    Alina Beygelzimer;Sham Kakade;John Langford

  • Opponent interactions between serotonin and dopamine

    Nathaniel D. Daw;Sham Kakade;Peter Dayan

  • Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

    N. Srinivas;A. Krause;S. M. Kakade;M. Seeger

  • Multi-view clustering via canonical correlation analysis

    Kamalika Chaudhuri;Sham M. Kakade;Karen Livescu;Karthik Sridharan

  • Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

    Niranjan Srinivas;Andreas Krause;Sham M. Kakade;Matthias Seeger

  • Approximately Optimal Approximate Reinforcement Learning

    Sham Kakade;John Langford

  • Stochastic Linear Optimization Under Bandit Feedback

    Varsha Dani;Thomas P Hayes;Sham M Kakade

  • How to escape saddle points efficiently

    Chi Jin;Rong Ge;Praneeth Netrapalli;Sham M. Kakade

  • Learning and selective attention.

    P Dayan;S Kakade;P R Montague

  • On the Sample Complexity of Reinforcement Learning

    Sham Machandranath Kakade

  • Dopamine: generalization and bonuses

    Sham Kakade;Peter Dayan

  • A spectral algorithm for learning Hidden Markov Models

    Daniel Hsu;Sham M. Kakade;Tong Zhang

  • Multi-Label Prediction via Compressed Sensing

    John Langford;Tong Zhang;Daniel J. Hsu;Sham M Kakade

  • Meta-Learning with Implicit Gradients

    Aravind Rajeswaran;Chelsea Finn;Sham M. Kakade;Sergey Levine

  • Multi-Label Prediction via Compressed Sensing

    Daniel Hsu;Sham M. Kakade;John Langford;Tong Zhang

  • A tail inequality for quadratic forms of subgaussian random vectors

    Daniel J. Hsu;Sham M. Kakade;Tong Zhang

  • Learning mixtures of spherical gaussians: moment methods and spectral decompositions

    Daniel Hsu;Sham M. Kakade

  • Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator.

    Maryam Fazel;Rong Ge;Sham M. Kakade;Mehran Mesbahi

  • Robust Aggregation for Federated Learning

    Krishna Pillutla;Sham M. Kakade;Zaid Harchaoui

Frequent Co-Authors

Daniel Hsu
Daniel Hsu Columbia University
Praneeth Netrapalli
Praneeth Netrapalli Google (United States)
Rong Ge
Rong Ge Duke University
Dean P. Foster
Dean P. Foster Amazon (United States)
Aaron Sidford
Aaron Sidford Stanford University
Prateek Jain
Prateek Jain Google (United States)
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Alekh Agarwal
Alekh Agarwal Google (United States)
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Ambuj Tewari
Ambuj Tewari University of Michigan–Ann Arbor

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