World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
91
Citations
32634
World Ranking
584
National Ranking
311

Jose M. F. Moura 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 Jose M. F. Moura 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: 643 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Jose M. F. Moura 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 Jose M. F. Moura 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: 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

  • 2014 - Fellow, National Academy of Inventors
  • 2013 - Member of the National Academy of Engineering For contributions to the theory and practice of statistical signal processing.
  • 2005 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 1994 - IEEE Fellow For contributions to nonlinear filtering and model-based signal processing.

Overview

Jose M. F. Moura is affiliated with Carnegie Mellon University in the United States and has focused their academic research primarily in the field of Computer Science.

Their work extensively covers the subfields of Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, and Education.

The main research topics addressed by Moura include:

  • Advanced Graph Neural Networks
  • Complex Network Analysis Techniques
  • Stochastic Gradient Optimization Techniques
  • Graph Theory and Algorithms
  • Sparse and Compressive Sensing Techniques
  • Neural Networks and Applications
  • Domain Adaptation and Few-Shot Learning

Their recent published papers include:

  • "Graph Signal Processing: History, development, impact, and outlook" (2023) in IEEE Signal Processing Magazine
  • "Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology" (2020) in IEEE Signal Processing Magazine
  • "Primal-Dual Methods for Large-Scale and Distributed Convex Optimization and Data Analytics" (2020) in Proceedings of the IEEE
  • "Graph Fourier Transform: A Stable Approximation" (2020) in IEEE Transactions on Signal Processing
  • "Graph Signal Processing: Foundations and Emerging Directions [From the Guest Editors]" (2020) in IEEE Signal Processing Magazine

Frequent co-authors working collaboratively with Moura include:

  • Joseph Lillie
  • Toshio Fukuda
  • Karen Hawkins
  • Kathleen Kramer
  • Stephen Phillips

Moura has published frequently in the following venues:

  • arXiv (Cornell University)
  • IEEE Transactions on Power Electronics
  • IEEE Signal Processing Magazine
  • IEEE Antennas and Wireless Propagation Letters
  • IEEE Transactions on Signal Processing

The researcher has been recognized with several awards including:

  • Fellow, National Academy of Inventors (2014)
  • Member of the National Academy of Engineering (2013) for contributions to the theory and practice of statistical signal processing
  • Fellow of the American Association for the Advancement of Science (AAAS) (2005)
  • IEEE Fellow (1994) for contributions to nonlinear filtering and model-based signal processing

Best Publications

  • Discrete Signal Processing on Graphs

    A. Sandryhaila;J. M. F. Moura

  • Graph Signal Processing: Overview, Challenges, and Applications

    Antonio Ortega;Pascal Frossard;Jelena Kovacevic;Jose M. F. Moura

  • SPIRAL: Code Generation for DSP Transforms

    M. Puschel;J.M.F. Moura;J.R. Johnson;D. Padua

  • Gossip Algorithms for Distributed Signal Processing

    Alexandros G Dimakis;Soummya Kar;José M F Moura;Michael G Rabbat

  • Discrete Signal Processing on Graphs: Frequency Analysis

    Aliaksei Sandryhaila;Jose M. F. Moura

  • Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure

    Aliaksei Sandryhaila;Jose M.F. Moura

  • Distributed Consensus Algorithms in Sensor Networks With Imperfect Communication: Link Failures and Channel Noise

    S. Kar;J.M.F. Moura

  • Fast Distributed Gradient Methods

    Dusan Jakovetic;Joao Xavier;Jose M. F. Moura

  • Visual Dialog

    Abhishek Das;Satwik Kottur;Khushi Gupta;Avi Singh

  • Distributing the Kalman Filter for Large-Scale Systems

    U.A. Khan;J.M.F. Moura

  • Explainable machine learning in deployment

    Umang Bhatt;Alice Xiang;Shubham Sharma;Adrian Weller

  • Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication

    Soummya Kar;J. M. F. Moura;K. Ramanan

  • Distributed Consensus Algorithms in Sensor Networks: Quantized Data and Random Link Failures

    S. Kar;J.M.F. Moura

  • Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

    Abhishek Das;Satwik Kottur;Jose M. F. Moura;Stefan Lee

  • Distributed Sensor Localization in Random Environments Using Minimal Number of Anchor Nodes

    U.A. Khan;S. Kar;J.M.F. Moura

  • Modeling of Future Cyber–Physical Energy Systems for Distributed Sensing and Control

    Marija D Ilić;Le Xie;Usman A Khan;José M F Moura

  • Adversarial Multiple Source Domain Adaptation

    Han Zhao;Shanghang Zhang;Guanhang Wu;José M. F. Moura

  • STACS: new active contour scheme for cardiac MR image segmentation

    C. Pluempitiwiriyawej;J.M.F. Moura;Yi-Jen Lin Wu;Chien Ho

  • Signal Recovery on Graphs: Variation Minimization

    Siheng Chen;Aliaksei Sandryhaila;Jose M. F. Moura;Jelena Kovacevic

  • Sensor Networks With Random Links: Topology Design for Distributed Consensus

    S. Kar;J.M.F. Moura

  • Special Issue on Program Generation, Optimization, and Platform Adaptation

    J.M.F. Moura;M. Puschel;D. Padua;J. Dongarra

Frequent Co-Authors

Soummya Kar
Soummya Kar Carnegie Mellon University
Usman A. Khan
Usman A. Khan Tufts University
H. Vincent Poor
H. Vincent Poor Princeton University
Bruno Sinopoli
Bruno Sinopoli Washington University in St. Louis
Aleksandar Kavcic
Aleksandar Kavcic University of Hawaii at Manoa
James H. Garrett
James H. Garrett Carnegie Mellon University
Jelena Kovacevic
Jelena Kovacevic New York University
Dhruv Batra
Dhruv Batra Georgia Institute of Technology
Lucio Soibelman
Lucio Soibelman University of Southern California

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