World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
12630
World Ranking
4073
National Ranking
64

Moshe Tennenholtz 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 Moshe Tennenholtz 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: 319 publications — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Moshe Tennenholtz 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 Moshe Tennenholtz 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: 56 D-Index — 72nd percentile

72% 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

  • 2019 - ACM Fellow For contributions to AI and algorithmic game theory
  • 2012 - ACM AAAI Allen Newell Award For fundamental contributions at the intersection of computer science, game theory, and economics, most particularly in multi-agent systems and social coordination (broadly construed), which have yielded major contributions to all three disciplines.
  • 2010 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions in the area of multiagent systems and beyond, and for extraordinary service to the AI community.

Overview

Moshe Tennenholtz is affiliated with the Technion - Israel Institute of Technology in Israel. Their research spans the fields of Decision Sciences and Computer Science, with significant contributions in subfields such as Management Science and Operations Research, Artificial Intelligence, Economics and Econometrics, Information Systems, and Marketing.

Their work focuses on a range of main topics that include:

  • Game Theory and Applications
  • Auction Theory and Applications
  • Game Theory and Voting Systems
  • Consumer Market Behavior and Pricing
  • Blockchain Technology Applications and Security
  • Privacy-Preserving Technologies in Data
  • Topic Modeling

Some of their recent papers are:

  • "Congestion Games with Agent Failures," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "PMI-Masking: Principled masking of correlated spans," 2020, arXiv (Cornell University)
  • "Competitive Search," 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
  • "Protecting the Protected Group: Circumventing Harmful Fairness," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Pareto-Improving Data-Sharing," 2022, 2022 ACM Conference on Fairness, Accountability, and Transparency

Frequent publication venues for their work include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Artificial Intelligence Research
  • Electronic Proceedings in Theoretical Computer Science
  • SSRN Electronic Journal

The scientist frequently collaborates with several co-authors, including:

  • Ronen Gradwohl
  • Yotam Gafni
  • Roi Reichart
  • Oren Kurland
  • Itai Arieli

Moshe Tennenholtz has received several awards recognizing their contributions, such as:

  • ACM Fellow, 2019, for contributions to AI and algorithmic game theory
  • ACM AAAI Allen Newell Award, 2012, for fundamental contributions at the intersection of computer science, game theory, and economics, with impact on multi-agent systems and social coordination
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 2010, for significant contributions in multiagent systems and exceptional service to the AI community

Best Publications

  • R-max - a general polynomial time algorithm for near-optimal reinforcement learning

    Ronen I. Brafman;Moshe Tennenholtz

  • On social laws for artificial agent societies: off-line design

    Yoav Shoham;Moshe Tennenholtz

  • On the Synthesis of Useful Social Laws for Artificial Agent Societies (Preliminary Report).

    Yoav Shoham;Moshe Tennenholtz

  • Approximate mechanism design without money

    Ariel D. Procaccia;Moshe Tennenholtz

  • On the synthesis of useful social laws for artificial agent societies

    Yoav Shoham;Moshe Tennenholtz

  • On the emergence of social conventions: modeling, analysis, and simulations

    Yoav Shoham;Moshe Tennenholtz

  • Trust-based recommendation systems: an axiomatic approach

    Reid Andersen;Christian Borgs;Jennifer Chayes;Uriel Feige

  • Adaptive load balancing: a study in multi-agent learning

    Andrea Schaerf;Yoav Shoham;Moshe Tennenholtz

  • Ranking systems: the PageRank axioms

    Alon Altman;Moshe Tennenholtz

  • Artificial social systems

    Yoram Moses;Moshe Tennenholtz

  • An Algorithm for Multi-Unit Combinatorial Auctions

    Kevin Leyton-Brown;Yoav Shoham;Moshe Tennenholtz

  • Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System

    Elad Yom-Tov;Guy Feraru;Mark Kozdoba;Shie Mannor

  • Emergent Conventions in Multi-Agent Systems: Initial Experimental Results and Observations (Preliminary Report).

    Yoav Shoham;Moshe Tennenholtz

  • Bundling Equilibrium in Combinatorial auctions

    Ron Holzman;Noa E. Kfir-Dahav;Dov Monderer;Moshe Tennenholtz

  • Approximately optimal mechanism design via differential privacy

    Kobbi Nissim;Rann Smorodinsky;Moshe Tennenholtz

  • Choosing social laws for multi-agent systems: minimality and simplicity

    David Fitoussi;Moshe Tennenholtz

  • Strategyproof Approximation of the Minimax on Networks

    Noga Alon;Noga Alon;Michal Feldman;Michal Feldman;Ariel D. Procaccia;Moshe Tennenholtz;Moshe Tennenholtz

  • A note on competitive diffusion through social networks

    Noga Alon;Michal Feldman;Ariel D. Procaccia;Moshe Tennenholtz

  • Some Tractable Combinatorial Auctions

    Moshe Tennenholtz

  • Strong and correlated strong equilibria in monotone congestion games

    Ola Rozenfeld;Moshe Tennenholtz

  • A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients.

    Irit Hochberg;Guy Feraru;Mark Kozdoba;Shie Mannor

Frequent Co-Authors

Yoav Shoham
Yoav Shoham Stanford University
Michal Feldman
Michal Feldman Tel Aviv University
Noga Alon
Noga Alon Tel Aviv University
Ronen I. Brafman
Ronen I. Brafman Ben-Gurion University of the Negev
Uriel Feige
Uriel Feige Weizmann Institute of Science
Kevin Leyton-Brown
Kevin Leyton-Brown University of British Columbia
Ariel D. Procaccia
Ariel D. Procaccia Harvard University
Adam Tauman Kalai
Adam Tauman Kalai Microsoft (United States)
Elad Yom-Tov
Elad Yom-Tov Microsoft (United States)
Gil Kalai
Gil Kalai Hebrew University of Jerusalem

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Related Online Degrees & Career Pathways

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Considering these pathways can expand your expertise and enhance your employability in today's tech-driven job market.

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