World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
34
Citations
83799
World Ranking
9106
National Ranking
2538

Daniel Ramage publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Daniel Ramage sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 55 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Daniel Ramage D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Daniel Ramage sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 34 D-Index — 7th percentile

7% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Daniel Ramage is affiliated with Google in the United States and focuses primarily on research in computer science with a specialization in artificial intelligence and related subfields. Their body of work spans topics such as privacy-preserving technologies, cryptography, adversarial robustness in machine learning, and blockchain technology applications and security.

The scientist has contributed notably to the field of federated learning and privacy. Their recent publications include:

  • "Advances and Open Problems in Federated Learning," 2020, Foundations and Trends® in Machine Learning
  • "Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning," 2022, 2022 IEEE Symposium on Security and Privacy (SP)
  • "Federated Learning and Privacy," 2021, Queue
  • "Federated learning and privacy," 2022, Communications of the ACM
  • "Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning," 2021, arXiv (Cornell University)

Key topics covered within Daniel Ramage's research include:

  • Privacy-Preserving Technologies in Data
  • Cryptography and Data Security
  • Adversarial Robustness in Machine Learning
  • Blockchain Technology Applications and Security
  • Privacy, Security, and Data Protection
  • Advanced Data Storage Technologies
  • Mobile Crowdsensing and Crowdsourcing

Their frequent co-authors, reflecting ongoing collaborations, are:

  • Peter Kairouz
  • Kallista Bonawitz
  • Hubert Eichner
  • Marco Gruteser
  • Adrià Gascón

Daniel Ramage has appeared in multiple publication venues, with a concentration in the following:

  • arXiv (Cornell University)
  • Foundations and Trends® in Machine Learning
  • 2022 IEEE Symposium on Security and Privacy (SP)
  • Queue
  • Communications of the ACM

Their academic contributions primarily focus on the computer science domain with research output concentrated in artificial intelligence, information systems, computer networks and communications, sociology and political science, and computer science applications, outlining a multidisciplinary approach in their work.

Best Publications

  • Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

    Paul Shannon;Andrew Markiel;Owen Ozier;Nitin S. Baliga

  • Communication-Efficient Learning of Deep Networks from Decentralized Data

    H. Brendan McMahan;Eider Moore;Daniel Ramage;Seth Hampson

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Practical Secure Aggregation for Privacy Preserving Machine Learning.

    Kallista A. Bonawitz;Vladimir Ivanov;Ben Kreuter;Antonio Marcedone

  • Towards Federated Learning at Scale: System Design

    Kallista A. Bonawitz;Hubert Eichner;Wolfgang Grieskamp;Dzmitry Huba

  • Federated Optimization: Distributed Machine Learning for On-Device Intelligence

    Jakub Konečný;H. Brendan McMahan;Daniel Ramage;Peter Richtarik

  • Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora

    Daniel Ramage;David Hall;Ramesh Nallapati;Christopher D. Manning

  • Federated Learning of Deep Networks using Model Averaging

    H. Brendan McMahan;Eider Moore;Daniel Ramage;Blaise Aguera y Arcas

  • Federated Learning for Mobile Keyboard Prediction

    Andrew Hard;Chloé M Kiddon;Daniel Ramage;Francoise Beaufays

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Characterizing Microblogs with Topic Models

    Daniel Ramage;Susan T. Dumais;Daniel J. Liebling

  • Learning Differentially Private Recurrent Language Models

    H. Brendan McMahan;Daniel Ramage;Kunal Talwar;Li Zhang

  • Federated Optimization: Distributed Optimization Beyond the Datacenter

    Jakub Konečný;H. Brendan McMahan;Daniel Ramage

  • Applied Federated Learning: Improving Google Keyboard Query Suggestions

    Timothy Yang;Galen Andrew;Hubert Eichner;Haicheng Sun

  • Social tag prediction

    Paul Heymann;Daniel Ramage;Hector Garcia-Molina

  • #TwitterSearch: a comparison of microblog search and web search

    Jaime Teevan;Daniel Ramage;Merredith Ringel Morris

  • Clustering the tagged web

    Daniel Ramage;Paul Heymann;Christopher D. Manning;Hector Garcia-Molina

  • Interpretation and trust: designing model-driven visualizations for text analysis

    Jason Chuang;Daniel Ramage;Christopher Manning;Jeffrey Heer

  • Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

    Unknown

  • Partially labeled topic models for interpretable text mining

    Daniel Ramage;Christopher D. Manning;Susan Dumais

  • Practical Secure Aggregation for Federated Learning on User-Held Data

    Kallista A. Bonawitz;Vladimir Ivanov;Ben Kreuter;Antonio Marcedone

Frequent Co-Authors

H. Brendan McMahan
H. Brendan McMahan Google (United States)
Christopher D. Manning
Christopher D. Manning Stanford University
Li Zhang
Li Zhang Google (United States)
Kunal Talwar
Kunal Talwar Apple (United States)
Dan Jurafsky
Dan Jurafsky Stanford University
Francoise Beaufays
Francoise Beaufays Google (United States)
Susan T. Dumais
Susan T. Dumais Microsoft (United States)
Farinaz Koushanfar
Farinaz Koushanfar University of California, San Diego
Dawn Song
Dawn Song University of California, Berkeley

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