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D-Index & Metrics

Computer Science

D-Index
46
Citations
58433
World Ranking
6628
National Ranking
2925

Abdel-rahman Mohamed 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 Abdel-rahman Mohamed 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: 97 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Abdel-rahman Mohamed 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 Abdel-rahman Mohamed 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: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Overview

Abdel-rahman Mohamed is affiliated with Facebook in the United States. Their research primarily focuses on computer science, with a specialization in artificial intelligence, signal processing, cognitive neuroscience, and experimental and cognitive psychology.

The scientist's work covers multiple topics including speech recognition and synthesis, music and audio processing, topic modeling, speech and audio processing, natural language processing techniques, hearing loss and rehabilitation, and neuroscience and music perception.

Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • 2022 IEEE Spoken Language Technology Workshop (SLT)
  • IEEE Signal Processing Magazine
  • ConductScience Proceedings

Recent papers authored by Abdel-rahman Mohamed involve research on speech representation learning, neural computations in auditory pathways, and language modeling in dialogue systems. Notable papers include:

  • "wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations," 2020, arXiv (Cornell University)
  • "HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units," 2021, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Dissecting neural computations in the human auditory pathway using deep neural networks for speech," 2023, Nature Neuroscience
  • "SUPERB: Speech processing Universal PERformance Benchmark," 2021, arXiv (Cornell University)
  • "Generative Spoken Dialogue Language Modeling," 2023, Transactions of the Association for Computational Linguistics

Abdel-rahman Mohamed has collaborated frequently with several researchers, including:

  • Shang-Wen Li
  • Shinji Watanabe
  • Hung-yi Lee
  • Wei-Ning Hsu
  • Kushal Lakhotia

The body of work spans over 44 publications in computer science-related fields, with 30 publications focusing on artificial intelligence and 14 in signal processing. The research reflects broad interdisciplinary engagement, contributing to improved understanding and technologies in speech and audio domains alongside cognitive neuroscience aspects.

Best Publications

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

    G. Hinton;Li Deng;Dong Yu;G. E. Dahl

  • Speech recognition with deep recurrent neural networks

    Alex Graves;Abdel-rahman Mohamed;Geoffrey Hinton

  • BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

    Mike Lewis;Yinhan Liu;Naman Goyal;Marjan Ghazvininejad

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition

    Geoffrey Hinton;Li Deng;Dong Yu;George Dahl

  • Convolutional neural networks for speech recognition

    Ossama Abdel-Hamid;Abdel-Rahman Mohamed;Hui Jiang;Li Deng

  • wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

    Alexei Baevski;Henry Zhou;Abdelrahman Mohamed;Michael Auli

  • Acoustic Modeling Using Deep Belief Networks

    A. Mohamed;G. E. Dahl;G. Hinton

  • Hybrid speech recognition with Deep Bidirectional LSTM

    Alex Graves;Navdeep Jaitly;Abdel-rahman Mohamed

  • Deep Convolutional Neural Networks for Large-scale Speech Tasks

    Tara N. Sainath;Brian Kingsbury;George Saon;Hagen Soltau

  • HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

    Wei-Ning Hsu;Benjamin Bolte;Yao-Hung Hubert Tsai;Kushal Lakhotia

  • Deep convolutional neural networks for LVCSR

    Tara N. Sainath;Abdel-rahman Mohamed;Brian Kingsbury;Bhuvana Ramabhadran

  • Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition

    Ossama Abdel-Hamid;Abdel-rahman Mohamed;Hui Jiang;Gerald Penn

  • wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

    Alexei Baevski;Yuhao Zhou;Abdelrahman Mohamed;Michael Auli

  • SUPERB: Speech processing Universal PERformance Benchmark

    Shu-wen Yang;Po-Han Chi;Yung-Sung Chuang;Cheng-I Jeff Lai

  • Unsupervised Cross-lingual Representation Learning for Speech Recognition

    Alexis Conneau;Alexei Baevski;Ronan Collobert;Abdelrahman Mohamed

  • Binary Coding of Speech Spectrograms Using a Deep Auto-encoder

    Li Deng;Michael L. Seltzer;Dong Yu;Alex Acero

  • Self-Supervised Speech Representation Learning: A Review

    Unknown

  • Libri-Light: A Benchmark for ASR with Limited or No Supervision

    J. Kahn;M. Riviere;W. Zheng;E. Kharitonov

  • Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine

    George Dahl;Marc'aurelio Ranzato;Abdel-rahman Mohamed;Geoffrey E. Hinton

  • Deep Belief Networks using discriminative features for phone recognition

    Abdel-rahman Mohamed;Tara N. Sainath;George Dahl;Bhuvana Ramabhadran

  • The shared views of four research groups )

    Geoffrey Hinton;Li Deng;Dong Yu;George E. Dahl

Frequent Co-Authors

Tara N. Sainath
Tara N. Sainath Google (United States)
Brian Kingsbury
Brian Kingsbury IBM (United States)
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Bhuvana Ramabhadran
Bhuvana Ramabhadran Google (United States)
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
George E. Dahl
George E. Dahl Google (United States)
Li Deng
Li Deng Citadel
Rich Caruana
Rich Caruana Microsoft (United States)
Matthew Richardson
Matthew Richardson Microsoft (United States)
Dong Yu
Dong Yu Tencent (China)

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