World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
50101
World Ranking
2975
National Ranking
1456

Mark Sandler 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 Mark Sandler 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: 522 publications — 94th percentile

94% of scientists in this discipline score the same or lower.

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

Mark Sandler 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 Mark Sandler 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: 61 D-Index — 79th percentile

79% 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

  • 2016 - Fellow of the Royal Academy of Engineering (UK)

Overview

Mark Sandler is affiliated with Google in the United States and has contributed extensively to the field of Computer Science, with a particular focus on Signal Processing, Computer Vision and Pattern Recognition, and Artificial Intelligence. Their work also touches on Cognitive Neuroscience and Music.

Sandler's research spans several key topics, including:

  • Music and Audio Processing
  • Music Technology and Sound Studies
  • Speech and Audio Processing
  • Domain Adaptation and Few-Shot Learning
  • Diverse Musicological Studies
  • Neural Networks and Applications
  • Advanced Neural Network Applications

Their recent papers demonstrate a strong engagement with both theoretical and applied aspects of these fields. Notable recent publications include:

  • "Fine-tuning Image Transformers using Learnable Memory," 2022, published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "A History of Audio Effects," 2020, published in Applied Sciences
  • "Statistical Deconvolution for Inference of Infection Time Series," 2022, published in Epidemiology
  • "The Challenge: From MPEG Intellectual Property Rights Ontologies to Smart Contracts and Blockchains [Standards in a Nutshell]," 2020, published in IEEE Signal Processing Magazine
  • "HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning," 2022, published on arXiv (Cornell University)

Sandler frequently collaborates with a set of co-authors, indicating a network of scholarly interaction primarily within their research domains. Frequent co-authors include:

  • Andrey Zhmoginov
  • Max Vladymyrov
  • Charalampos Saitis
  • Emmanouil Benetos
  • György Fazekas

The venues where Sandler publishes reflect the interdisciplinary nature of their work, with significant contributions to:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • The Journal of Sexual Medicine
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of the Audio Engineering Society

Mark Sandler's recognition includes election as a Fellow of the Royal Academy of Engineering (UK) in 2016, marking a noted distinction in their professional career.

Best Publications

  • MobileNetV2: Inverted Residuals and Linear Bottlenecks

    Mark Sandler;Andrew Howard;Menglong Zhu;Andrey Zhmoginov

  • Searching for MobileNetV3

    Andrew Howard;Ruoming Pang;Hartwig Adam;Quoc Le

  • MnasNet: Platform-Aware Neural Architecture Search for Mobile

    Mingxing Tan;Bo Chen;Ruoming Pang;Vijay Vasudevan

  • A tutorial on onset detection in music signals

    J.P. Bello;L. Daudet;S. Abdallah;C. Duxbury

  • Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

    Andrew Howard;Andrey Zhmoginov;Liang-Chieh Chen;Mark Sandler

  • Searching for MobileNetV3.

    Andrew Howard;Mark Sandler;Grace Chu;Liang-Chieh Chen

  • Convolutional recurrent neural networks for music classification

    Keunwoo Choi;Gyorgy Fazekas;Mark Sandler;Kyunghyun Cho

  • NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

    Tien-Ju Yang;Andrew G. Howard;Bo Chen;Xiao Zhang

  • Sonic visualiser: an open source application for viewing, analysing, and annotating music audio files

    Chris Cannam;Christian Landone;Mark Sandler

  • The Music Ontology.

    Yves Raimond;Samer A. Abdallah;Mark B. Sandler;Frederick Giasson

  • Detecting harmonic change in musical audio

    Christopher Harte;Mark Sandler;Martin Gasser

  • Automatic Tagging Using Deep Convolutional Neural Networks.

    Keunwoo Choi;György Fazekas;Mark B. Sandler

  • Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations.

    Christopher Harte;Mark B. Sandler;Samer A. Abdallah;Emilia Gómez

  • On the use of phase and energy for musical onset detection in the complex domain

    J.P. Bello;C. Duxbury;M. Davies;M. Sandler

  • Structural Segmentation of Musical Audio by Constrained Clustering

    M. Levy;M. Sandler

  • Classification of audio signals using statistical features on time and wavelet transform domains

    T. Lambrou;P. Kudumakis;R. Speller;M. Sandler

  • Automatic Chord Identifcation using a Quantised Chromagram

    Christopher Harte;Mark Sandler

  • Organizing search results in a topic hierarchy

    Mark M. Sandler;Kushal Dave

  • Automatic Interlinking of Music Datasets on the Semantic Web.

    Yves Raimond;Christopher Sutton;Mark B. Sandler

  • Complex domain onset detection for musical signals

    Chris Duxbury;Juan Pablo Bello;Mike Davies;Mark Sandler

Frequent Co-Authors

Juan Pablo Bello
Juan Pablo Bello New York University
Kyunghyun Cho
Kyunghyun Cho New York University
Simon Dixon
Simon Dixon Queen Mary University of London
Mark D. Plumbley
Mark D. Plumbley King's College London
Luciano da Fontoura Costa
Luciano da Fontoura Costa Universidade de São Paulo
Geraint A. Wiggins
Geraint A. Wiggins Vrije Universiteit Brussel
Laurent Daudet
Laurent Daudet Université Paris Cité
Jon Kleinberg
Jon Kleinberg Cornell University
Bo Chen
Bo Chen Xidian University

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