World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
5764
World Ranking
10282
National Ranking
36

Marco Canini 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 Marco Canini 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: 177 publications — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Marco Canini 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 Marco Canini 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Marco Canini is affiliated with King Abdullah University of Science and Technology in Saudi Arabia. Their research primarily falls within the broader field of Computer Science, with focused contributions in Artificial Intelligence, Computer Networks and Communications, and Information Systems. Additional areas of study include Computational Mechanics and Computer Vision and Pattern Recognition.

The scientist has authored numerous publications, including recent papers such as "P4xos: Consensus as a Network Service" (2020) published in IEEE/ACM Transactions on Networking, "A Comprehensive Empirical Study of Heterogeneity in Federated Learning" (2023) in IEEE Internet of Things Journal, and "An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems" (2021) on arXiv. Other papers include "Rethinking gradient sparsification as total error minimization" (2021) also on arXiv and "Do the best cloud configurations grow on trees?" (2020) in the Proceedings of the VLDB Endowment.

Research topics addressed by Marco Canini encompass:

  • Privacy-Preserving Technologies in Data
  • Stochastic Gradient Optimization Techniques
  • Cloud Computing and Resource Management
  • Distributed systems and fault tolerance
  • Sparse and Compressive Sensing Techniques
  • IoT and Edge/Fog Computing
  • Recommender Systems and Techniques

Frequent co-authors include:

  • Ahmed M. Abdelmoniem
  • Salma Kharrat
  • Peter Richtárik
  • Samuel Horváth
  • Chen-Yu Ho

Their work has appeared in various reputable publication venues, with notable frequency in:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Networking
  • IEEE Internet of Things Journal
  • Proceedings of the VLDB Endowment
  • Proceedings of the AAAI Conference on Artificial Intelligence

Best Publications

  • A NICE way to test openflow applications

    Marco Canini;Daniele Venzano;Peter Perešíni;Dejan Kostić

  • Sonata: query-driven streaming network telemetry

    Arpit Gupta;Rob Harrison;Marco Canini;Nick Feamster

  • In-Network Computation is a Dumb Idea Whose Time Has Come

    Amedeo Sapio;Ibrahim Abdelaziz;Abdulla Aldilaijan;Marco Canini

  • FatTire: declarative fault tolerance for software-defined networks

    Mark Reitblatt;Marco Canini;Arjun Guha;Nate Foster

  • Efficient application identification and the temporal and spatial stability of classification schema

    Wei Li;Marco Canini;Andrew W. Moore;Raffaele Bolla

  • C3: cutting tail latency in cloud data stores via adaptive replica selection

    Lalith Suresh;Marco Canini;Stefan Schmid;Anja Feldmann

  • NetPaxos: consensus at network speed

    Huynh Tu Dang;Daniele Sciascia;Marco Canini;Fernando Pedone

  • Panopticon: reaping the benefits of incremental SDN deployment in enterprise networks

    Dan Levin;Marco Canini;Stefan Schmid;Fabian Schaffert

  • Scaling Distributed Machine Learning with In-Network Aggregation

    Amedeo Sapio;Marco Canini;Chen-Yu Ho;Jacob Nelson

  • A SOFT way for openflow switch interoperability testing

    Maciej Kuzniar;Peter Peresini;Marco Canini;Daniele Venzano

  • A distributed and robust SDN control plane for transactional network updates

    Marco Canini;Petr Kuznetsov;Dan Levin;Stefan Schmid

  • Paxos Made Switch-y

    Huynh Tu Dang;Marco Canini;Fernando Pedone;Robert Soulé

  • Identifying and using energy-critical paths

    Nedeljko Vasić;Prateek Bhurat;Dejan Novaković;Marco Canini

  • Scaling Distributed Machine Learning with In-Network Aggregation

    Amedeo Sapio;Marco Canini;Chen-Yu Ho;Jacob Nelson

  • Natural Compression for Distributed Deep Learning

    Samuel Horváth;Chen-Yu Ho;Ludovit Horváth;Atal Narayan Sahu

  • Efficient sparse collective communication and its application to accelerate distributed deep learning

    Jiawei Fei;Chen-Yu Ho;Atal N. Sahu;Marco Canini

  • Insomnia in the access: or how to curb access network related energy consumption

    Eduard Goma;Marco Canini;Alberto Lopez Toledo;Nikolaos Laoutaris

  • P4xos: Consensus as a Network Service

    Huynh Tu Dang;Pietro Bressana;Han Wang;Ki Suh Lee

  • Software transactional networking: concurrent and consistent policy composition

    Marco Canini;Petr Kuznetsov;Dan Levin;Stefan Schmid

  • An Industrial-Scale Software Defined Internet Exchange Point

    Arpit Gupta;Robert MacDavid;Rüdiger Birkner;Marco Canini

  • Incremental SDN deployment in enterprise networks

    Dan Levin;Marco Canini;Stefan Schmid;Anja Feldmann

  • LineFS: Efficient SmartNIC Offload of a Distributed File System with Pipeline Parallelism

    Jongyul Kim;Insu Jang;Waleed Reda;Jaeseong Im

Frequent Co-Authors

Dejan Kostic
Dejan Kostic Royal Institute of Technology
Stefan Schmid
Stefan Schmid Technical University of Berlin
Panos Kalnis
Panos Kalnis King Abdullah University of Science and Technology
Jennifer Rexford
Jennifer Rexford Princeton University
Anja Feldmann
Anja Feldmann Max Planck Society
Fernando Pedone
Fernando Pedone Universita della Svizzera Italiana
Nick Feamster
Nick Feamster University of Chicago
Arvind Krishnamurthy
Arvind Krishnamurthy University of Washington
Peter Richtárik
Peter Richtárik King Abdullah University of Science and Technology
Walter Willinger
Walter Willinger NIKSUN, Inc.

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