World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
91
Citations
36974
World Ranking
578
National Ranking
308

Phillip B. Gibbons 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 Phillip B. Gibbons 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: 293 publications — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Phillip B. Gibbons 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 Phillip B. Gibbons 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: 91 D-Index — 96th percentile

96% 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 Paris Kanellakis Theory and Practice Award For seminal work on the foundations of streaming algorithms and their application to large scale data analytics.
  • 2014 - IEEE Fellow For contributions to parallel computing and databases
  • 2006 - ACM Fellow For contributions to parallel computing, databases, and sensor networks.

Overview

Phillip B. Gibbons is affiliated with Carnegie Mellon University in the United States and has made contributions primarily in the field of Computer Science, with a significant focus on its various subfields, including Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, Computer Vision and Pattern Recognition, and Electrical and Electronic Engineering.

Their research covers main topics such as Parallel Computing and Optimization Techniques, Advanced Data Storage Technologies, Advanced Neural Network Applications, Advanced Memory and Neural Computing, Robotics and Sensor-Based Localization, Caching and Content Delivery, and Distributed Systems and Fault Tolerance.

Phillip B. Gibbons has authored numerous papers published in prominent venues. Selected recent works include:

  • "Advances and Open Problems in Federated Learning" (2020, Foundations and Trends® in Machine Learning)
  • "PIM-Tree" (2022, Proceedings of the VLDB Endowment)
  • "Sage" (2020, Proceedings of the VLDB Endowment)
  • "Federated Learning under Distributed Concept Drift" (2022, arXiv (Cornell University))
  • "Cortex: A Compiler for Recursive Deep Learning Models" (2020, arXiv (Cornell University))

The frequent co-authors collaborating with Phillip B. Gibbons include Charles McGuffey, Guy E. Blelloch, Laxman Dhulipala, Pratik Fegade, and Tianqi Chen.

The scientist's publications are most often found in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • Zenodo (CERN European Organization for Nuclear Research)
  • Foundations and Trends® in Machine Learning
  • Proceedings of the ACM on Measurement and Analysis of Computing Systems

Phillip B. Gibbons has received several awards recognizing specific contributions to their fields. These include the ACM Paris Kanellakis Theory and Practice Award in 2019 for foundational work in streaming algorithms applied to large-scale data analytics, being named an IEEE Fellow in 2014 for contributions to parallel computing and databases, and the ACM Fellow distinction in 2006 for contributions spanning parallel computing, databases, and sensor networks.

Best Publications

  • Advances and Open Problems in Federated Learning

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

  • Memory consistency and event ordering in scalable shared-memory multiprocessors

    Kourosh Gharachorloo;Daniel Lenoski;James Laudon;Phillip Gibbons

  • LOCI: fast outlier detection using the local correlation integral

    S. Papadimitriou;H. Kitagawa;P.B. Gibbons;C. Faloutsos

  • Advances and open problems in federated learning

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

  • SybilGuard: defending against sybil attacks via social networks

    Haifeng Yu;Michael Kaminsky;Phillip B. Gibbons;Abraham Flaxman

  • Method for reading server site anonymously

    Eran Gabber;Phillip B Gibbons;Yossi Matias;Alain J Mayer

  • SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks

    Haifeng Yu;P.B. Gibbons;M. Kaminsky;Feng Xiao

  • Synopsis diffusion for robust aggregation in sensor networks

    Suman Nath;Phillip B. Gibbons;Srinivasan Seshan;Zachary Anderson

  • More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

    Qirong Ho;James Cipar;Henggang Cui;Seunghak Lee

  • PipeDream: generalized pipeline parallelism for DNN training

    Deepak Narayanan;Aaron Harlap;Amar Phanishayee;Vivek Seshadri

  • IrisNet: an architecture for a worldwide sensor Web

    P.B. Gibbons;B. Karp;Y. Ke;S. Nath

  • Synopsis diffusion for robust aggregation in sensor networks

    Suman Nath;Phillip B. Gibbons;Srinivasan Seshan;Zachary R. Anderson

  • New sampling-based summary statistics for improving approximate query answers

    Phillip B. Gibbons;Yossi Matias

  • Fast incremental maintenance of approximate histograms

    Phillip B. Gibbons;Yossi Matias;Viswanath Poosala

  • Join synopses for approximate query answering

    Swarup Acharya;Phillip B. Gibbons;Viswanath Poosala;Sridhar Ramaswamy

  • Ambit: in-memory accelerator for bulk bitwise operations using commodity DRAM technology

    Vivek Seshadri;Donghyuk Lee;Thomas Mullins;Hasan Hassan

  • System for scheduling and controlling distribution of advertisement over communication network

    Micah A Adler;Phillip B Gibbons;Yossi Matias;ビー.ギボンズ フィリップ

  • Base-delta-immediate compression: practical data compression for on-chip caches

    Gennady Pekhimenko;Vivek Seshadri;Onur Mutlu;Michael A. Kozuch

  • RowClone: fast and energy-efficient in-DRAM bulk data copy and initialization

    Vivek Seshadri;Yoongu Kim;Chris Fallin;Donghyuk Lee

  • SybilGuard: defending against sybil attacks via social networks

    Haifeng Yu;Michael Kaminsky;Phillip B. Gibbons;Abraham D. Flaxman

  • The Non-IID Data Quagmire of Decentralized Machine Learning

    Kevin Hsieh;Amar Phanishayee;Onur Mutlu;Phillip Gibbons

  • Retrospective: memory consistency and event ordering in scalable shared-memory multiprocessors

    Kourosh Gharachorloo

Frequent Co-Authors

Yossi Matias
Yossi Matias Google (United States)
Todd C. Mowry
Todd C. Mowry Carnegie Mellon University
Guy E. Blelloch
Guy E. Blelloch Carnegie Mellon University
Michael Kozuch
Michael Kozuch Intel (United States)
Suman Nath
Suman Nath Microsoft (United States)
Onur Mutlu
Onur Mutlu ETH Zurich
Srinivasan Seshan
Srinivasan Seshan Carnegie Mellon University
Vijaya Ramachandran
Vijaya Ramachandran The University of Texas at Austin
Gennady Pekhimenko
Gennady Pekhimenko University of Toronto

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