World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
25663
World Ranking
5199
National Ranking
239

Moritz Hardt 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 Moritz Hardt 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: 101 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

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

Moritz Hardt 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 Moritz Hardt 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: 51 D-Index — 63rd percentile

63% 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 - Fellow of Alfred P. Sloan Foundation

Overview

Moritz Hardt is affiliated with the Max Planck Institute for Intelligent Systems in Germany, focusing primarily on computer science. Their research spans various subfields including artificial intelligence, management science and operations research, safety research, economics and econometrics, and statistics and probability.

The scientist's work covers a range of main topics, notably:

  • Advanced Bandit Algorithms Research
  • Ethics and Social Impacts of AI
  • Explainable Artificial Intelligence (XAI)
  • Adversarial Robustness in Machine Learning
  • Reinforcement Learning in Robotics
  • Machine Learning and Data Classification
  • Domain Adaptation and Few-Shot Learning

Moritz Hardt has contributed papers to several publication venues, with a strong presence on arXiv (Cornell University), and additional publications in the 2022 ACM Conference on Fairness, Accountability, and Transparency, Communications of the ACM, Proceedings of the National Academy of Sciences, and the SSRN Electronic Journal. Key recent papers include:

  • Understanding deep learning (still) requires rethinking generalization, 2021, Communications of the ACM
  • Algorithmic amplification of politics on Twitter, 2021, Proceedings of the National Academy of Sciences
  • Stochastic Optimization for Performative Prediction, 2020, arXiv (Cornell University)
  • Difficult Lessons on Social Prediction from Wisconsin Public Schools, 2023, arXiv (Cornell University)
  • Questioning the Survey Responses of Large Language Models, 2023, arXiv (Cornell University)

The scientist frequently collaborates with a number of coauthors, including:

  • Celestine Mendler-Dünner
  • Rediet Abebe
  • Ricardo Dominguez-Olmedo
  • John A. Miller
  • Ludwig Schmidt

In recognition of their contributions, Moritz Hardt was named a Fellow of the Alfred P. Sloan Foundation in 2019.

Best Publications

  • Understanding deep learning (still) requires rethinking generalization

    Chiyuan Zhang;Samy Bengio;Moritz Hardt;Benjamin Recht

  • Fairness through awareness

    Cynthia Dwork;Moritz Hardt;Toniann Pitassi;Omer Reingold

  • Understanding deep learning requires rethinking generalization.

    Chiyuan Zhang;Samy Bengio;Moritz Hardt;Benjamin Recht

  • Equality of opportunity in supervised learning

    Moritz Hardt;Eric Price;Nathan Srebro

  • Sanity Checks for Saliency Maps

    Julius Adebayo;Justin Gilmer;Michael Christoph Muelly;Ian Goodfellow

  • Train faster, generalize better: stability of stochastic gradient descent

    Moritz Hardt;Benjamin Recht;Yoram Singer

  • Avoiding Discrimination through Causal Reasoning

    Niki Kilbertus;Mateo Rojas-Carulla;Giambattista Parascandolo;Moritz Hardt

  • On the geometry of differential privacy

    Moritz Hardt;Kunal Talwar

  • A Simple and Practical Algorithm for Differentially Private Data Release

    Moritz Hardt;Katrina Ligett;Frank Mcsherry

  • A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis

    Moritz Hardt;Guy N. Rothblum

  • The reusable holdout: Preserving validity in adaptive data analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Preserving Statistical Validity in Adaptive Data Analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Delayed Impact of Fair Machine Learning.

    Lydia T. Liu;Sarah Dean;Esther Rolf;Max Simchowitz

  • Measuring the predictability of life outcomes with a scientific mass collaboration.

    Matthew J Salganik;Ian Lundberg;Alexander T Kindel;Caitlin E Ahearn

  • Understanding Alternating Minimization for Matrix Completion

    Moritz Hardt

  • Identity Matters in Deep Learning

    Moritz Hardt;Tengyu Ma

  • Privately Releasing Conjunctions and the Statistical Query Barrier

    Anupam Gupta;Moritz Hardt;Aaron Roth;Jonathan R. Ullman

  • Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

    Yu Sun;Xiaolong Wang;Zhuang Liu;John Miller

  • Strategic Classification

    Moritz Hardt;Nimrod Megiddo;Christos Papadimitriou;Mary Wootters

  • Gradient Descent Learns Linear Dynamical Systems

    Moritz Hardt;Tengyu Ma;Benjamin Recht

  • A System for Massively Parallel Hyperparameter Tuning

    Liam Li;Kevin G. Jamieson;Afshin Rostamizadeh;Ekaterina Gonina

  • A System for Massively Parallel Hyperparameter Tuning

    Liam Li;Kevin Jamieson;Afshin Rostamizadeh;Ekaterina Gonina

  • Performative Prediction

    Juan C. Perdomo;Tijana Zrnic;Celestine Mendler-Dünner;Moritz Hardt

Frequent Co-Authors

Aaron Roth
Aaron Roth University of Pennsylvania
Benjamin Recht
Benjamin Recht University of California, Berkeley
Vitaly Feldman
Vitaly Feldman Apple (United States)
Omer Reingold
Omer Reingold Stanford University
Toniann Pitassi
Toniann Pitassi Columbia University
Cynthia Dwork
Cynthia Dwork Harvard University
Boaz Barak
Boaz Barak Harvard University
David Steurer
David Steurer ETH Zurich
Jonathan Ullman
Jonathan Ullman Northeastern University
Samy Bengio
Samy Bengio Apple (United States)

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