World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
16497
World Ranking
3995
National Ranking
1903

Aaron Roth 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 Aaron Roth 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: 165 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Aaron Roth 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 Aaron Roth 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

  • 2015 - Fellow of Alfred P. Sloan Foundation

Overview

Aaron Roth is affiliated with the University of Pennsylvania in the United States. Their research primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence and related subfields. Their work also intersects with Management Science and Operations Research, Economics and Econometrics, Sociology and Political Science, and Computational Theory and Mathematics.

Roth's recent published papers include:

  • A snapshot of the frontiers of fairness in machine learning, 2020, Communications of the ACM
  • The Ethical Algorithm: The Science of Socially Aware Algorithm Design, 2021, Perspectives on Science and Christian Faith
  • Descent-to-Delete: Gradient-Based Methods for Machine Unlearning, 2020, arXiv (Cornell University)
  • Adaptive Machine Unlearning, 2021, arXiv (Cornell University)
  • Local Differential Privacy for Evolving Data, 2020, Journal of Privacy and Confidentiality

They have collaborated frequently with several researchers, including Michael Kearns, Zhiwei Steven Wu, Natalie Collina, Saeed Sharifi-Malvajerdi, and Georgy Noarov.

Roth's work has been published extensively in various venues, some of the most frequent being:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • 2022 ACM Conference on Fairness, Accountability, and Transparency
  • Proceedings of the National Academy of Sciences
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)

Their research topics include:

  • Privacy-Preserving Technologies in Data
  • Adversarial Robustness in Machine Learning
  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Stochastic Gradient Optimization Techniques
  • Cryptography and Data Security
  • Explainable Artificial Intelligence (XAI)

Aaron Roth has been recognized as a Fellow of the Alfred P. Sloan Foundation in 2015.

Best Publications

  • The Algorithmic Foundations of Differential Privacy

    Cynthia Dwork;Aaron Roth

  • A learning theory approach to noninteractive database privacy

    Avrim Blum;Katrina Ligett;Aaron Roth

  • A learning theory approach to non-interactive database privacy

    Avrim Blum;Katrina Ligett;Aaron Roth

  • Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

    Michael J. Kearns;Seth Neel;Aaron Roth;Zhiwei Steven Wu

  • Selling privacy at auction

    Arpita Ghosh;Aaron Roth

  • The reusable holdout: Preserving validity in adaptive data analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Fairness in learning: classic and contextual bandits

    Matthew Joseph;Michael Kearns;Jamie Morgenstern;Aaron Roth

  • Preserving Statistical Validity in Adaptive Data Analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • A snapshot of the frontiers of fairness in machine learning

    Alexandra Chouldechova;Aaron Roth

  • Differential Privacy: An Economic Method for Choosing Epsilon

    Justin Hsu;Marco Gaboardi;Andreas Haeberlen;Sanjeev Khanna

  • The Frontiers of Fairness in Machine Learning

    Alexandra Chouldechova;Aaron Roth

  • Interactive privacy via the median mechanism

    Aaron Roth;Tim Roughgarden

  • Privately Releasing Conjunctions and the Statistical Query Barrier

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

  • A Convex Framework for Fair Regression

    Richard Berk;Hoda Heidari;Shahin Jabbari;Matthew Joseph

  • Constrained non-monotone submodular maximization: offline and secretary algorithms

    Anupam Gupta;Aaron Roth;Grant Schoenebeck;Kunal Talwar

  • Iterative constructions and private data release

    Anupam Gupta;Aaron Roth;Jonathan Ullman

  • Generalization in adaptive data analysis and holdout reuse

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Regret minimization and the price of total anarchy

    Avrim Blum;MohammadTaghi Hajiaghayi;Katrina Ligett;Aaron Roth

  • Differentially private combinatorial optimization

    Anupam Gupta;Katrina Ligett;Frank McSherry;Aaron Roth

  • An Empirical Study of Rich Subgroup Fairness for Machine Learning

    Michael Kearns;Seth Neel;Aaron Roth;Zhiwei Steven Wu

Frequent Co-Authors

Michael Kearns
Michael Kearns University of Pennsylvania
Jonathan Ullman
Jonathan Ullman Northeastern University
Sampath Kannan
Sampath Kannan University of Pennsylvania
Anupam Gupta
Anupam Gupta Carnegie Mellon University
Moritz Hardt
Moritz Hardt Max Planck Institute for Intelligent Systems
Cynthia Dwork
Cynthia Dwork Harvard University
Kunal Talwar
Kunal Talwar Apple (United States)
Rakesh Vohra
Rakesh Vohra University of Pennsylvania
Omer Reingold
Omer Reingold Stanford University
Andreas Haeberlen
Andreas Haeberlen University of Pennsylvania

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