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Computer Science
USA
2026

D-Index & Metrics

Best Female Scientists

D-Index
143
Citations
96772
World Ranking
202
National Ranking
127

Computer Science

D-Index
143
Citations
97365
World Ranking
53
National Ranking
30

Dawn Song 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 Dawn Song 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 413 publications — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Dawn Song 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 Dawn Song sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 143 D-Index — 100th percentile

100% 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

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Best Female Scientists Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2019 - ACM Fellow For contributions to security and privacy
  • 2010 - Fellow of the MacArthur Foundation
  • 2010 - Fellow of John Simon Guggenheim Memorial Foundation
  • 2007 - Fellow of Alfred P. Sloan Foundation

Overview

Dawn Song is affiliated with the University of California, Berkeley, specializing in computer science with a focus on artificial intelligence and information systems. Their research spans a variety of subfields, including computer vision and pattern recognition, signal processing, and intersections with sociology and political science.

Their scholarly output includes over 290 publications primarily in the field of computer science. Within this domain, they have concentrated on topics such as privacy-preserving technologies in data, adversarial robustness in machine learning, and topic modeling. Additional research themes cover advanced malware detection techniques, blockchain technology applications and security, natural language processing techniques, and broader areas in privacy, security, and data protection.

Dawn Song has contributed to numerous frequent publication venues, particularly:

  • arXiv (Cornell University)
  • IEEE Security & Privacy
  • Proceedings on Privacy Enhancing Technologies
  • Science
  • Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security

Frequently collaborating with other researchers, Song's common coauthors include Dan Hendrycks, Chenguang Wang, Vivek Nair, Jacob Steinhardt, and Chulin Xie.

Recent notable papers include:

  • "Advances and Open Problems in Federated Learning" (2020), published in Foundations and Trends® in Machine Learning
  • "The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization" (2021), presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Extracting Training Data from Large Language Models" (2020), available on arXiv (Cornell University)
  • "Measuring Mathematical Problem Solving With the MATH Dataset" (2021), available on arXiv (Cornell University)
  • "Measuring Massive Multitask Language Understanding" (2020), available on arXiv (Cornell University)

Dawn Song's work has been recognized through several fellowships, including:

  • ACM Fellow (2019), for contributions to security and privacy
  • Fellow of the MacArthur Foundation (2010)
  • Fellow of John Simon Guggenheim Memorial Foundation (2010)
  • Fellow of Alfred P. Sloan Foundation (2007)

Best Publications

  • Practical techniques for searches on encrypted data

    Dawn Xiaoding Song;D. Wagner;A. Perrig

  • Advances and Open Problems in Federated Learning

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

  • Random key predistribution schemes for sensor networks

    Haowen Chan;A. Perrig;D. Song

  • Provable Data Possession at Untrusted Stores.

    Giuseppe Ateniese;Randal C. Burns;Reza Curtmola;Joseph Herring

  • Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software

    James Newsome;Dawn Xiaodong Song

  • The Sybil attack in sensor networks: analysis & defenses

    James Newsome;Elaine Shi;Dawn Song;Adrian Perrig

  • Android permissions demystified

    Adrienne Porter Felt;Erika Chin;Steve Hanna;Dawn Song

  • Robust Physical-World Attacks on Deep Learning Visual Classification

    Kevin Eykholt;Ivan Evtimov;Earlence Fernandes;Bo Li

  • On Scaling Decentralized Blockchains

    Kyle Croman;Christian Decker;Ittay Eyal;Adem Efe Gencer

  • Efficient authentication and signing of multicast streams over lossy channels

    A. Perrig;R. Canetti;J.D. Tygar;Dawn Song

  • Advanced and authenticated marking schemes for IP traceback

    Dawn Xiaodong Song;A. Perrig

  • The TESLA Broadcast Authentication Protocol

    Adrian Perrig;Ran Canetti;J. D. Tygar;Dawn Song

  • Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

    Xinyun Chen;Chang Liu;Bo Li;Kimberly Lu

  • Polygraph: automatically generating signatures for polymorphic worms

    J. Newsome;B. Karp;D. Song

  • Model-Contrastive Federated Learning

    Qinbin Li;Bingsheng He;Dawn Song

  • Delving into Transferable Adversarial Examples and Black-box Attacks

    Yanpei Liu;Xinyun Chen;Chang Liu;Dawn Song

  • Advances and open problems in federated learning

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

  • SIA: secure information aggregation in sensor networks

    Bartosz Przydatek;Dawn Song;Adrian Perrig

  • Semantics-aware malware detection

    M. Christodorescu;S. Jha;S.A. Seshia;D. Song

  • Panorama: capturing system-wide information flow for malware detection and analysis

    Heng Yin;Dawn Song;Manuel Egele;Christopher Kruegel

  • BitBlaze: A New Approach to Computer Security via Binary Analysis

    Dawn Song;David Brumley;Heng Yin;Juan Caballero

Frequent Co-Authors

Elaine Shi
Elaine Shi Carnegie Mellon University
Adrian Perrig
Adrian Perrig ETH Zurich
David Brumley
David Brumley Carnegie Mellon University
Juan Caballero
Juan Caballero Madrid Institute for Advanced Studies
Heng Yin
Heng Yin University of California, Riverside
Prateek Saxena
Prateek Saxena National University of Singapore
Michael K. Reiter
Michael K. Reiter Duke University
Neil Zhenqiang Gong
Neil Zhenqiang Gong Duke University
Prateek Mittal
Prateek Mittal Princeton University
Krste Asanovic
Krste Asanovic University of California, Berkeley

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