World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
12222
World Ranking
5802
National Ranking
2637

Mark Hasegawa-Johnson 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 Hasegawa-Johnson 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: 396 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Mark Hasegawa-Johnson 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 Hasegawa-Johnson 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: 49 D-Index — 60th percentile

60% 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

  • 2020 - IEEE Fellow For contributions to speech processing of under-resourced languages
  • 2009 - ACM Senior Member

Overview

Mark Hasegawa-Johnson is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their primary research contributions are in the field of computer science, with a focus on artificial intelligence, signal processing, and computer vision and pattern recognition. A smaller portion of their work also touches on pharmacy and experimental and cognitive psychology.

The scientist's work covers several main topics related to speech and audio processing. These include:

  • Speech Recognition and Synthesis
  • Speech and Audio Processing
  • Music and Audio Processing
  • Natural Language Processing Techniques
  • Topic Modeling
  • Multimodal Machine Learning Applications
  • Infant Health and Development

Selected recent publications by Mark Hasegawa-Johnson include the following:

  • "Unsupervised Speech Decomposition via Triple Information Bottleneck," 2020, published in arXiv (Cornell University)
  • "SpeechSplit2.0: Unsupervised Speech Disentanglement for Voice Conversion without Tuning Autoencoder Bottlenecks," 2022, presented at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition," 2022, presented at Interspeech 2022
  • "ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers," 2022, published in arXiv (Cornell University)
  • "Speech Technology for Unwritten Languages," 2020, published in IEEE/ACM Transactions on Audio Speech and Language Processing

Frequent co-authors collaborating with Mark Hasegawa-Johnson include:

  • Chang D. Yoo
  • Kaizhi Qian
  • Jialu Li
  • Nancy L. McElwain
  • John Harvill

Publication venues where the scientist most often publishes are:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • Interspeech 2022
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Speech Communication

The scientist has received professional recognitions including being named an IEEE Fellow in 2020 for contributions to speech processing of under-resourced languages. They were also recognized as an ACM Senior Member in 2009.

Best Publications

  • Semantic Image Inpainting with Deep Generative Models

    Raymond A. Yeh;Chen Chen;Teck Yian Lim;Alexander G. Schwing;Alexander G. Schwing

  • Joint optimization of masks and deep recurrent neural networks for monaural source separation

    Po-Sen Huang;Minje Kim;Mark Hasegawa-Johnson;Paris Smaragdis

  • Deep learning for monaural speech separation

    Po Sen Huang;Minje Kim;Mark Hasegawa-Johnson;Paris Smaragdis

  • Semantic Image Inpainting with Perceptual and Contextual Losses.

    Raymond A. Yeh;Chen Chen;Teck-Yian Lim;Mark Hasegawa-Johnson

  • Brain anatomy differences in childhood stuttering.

    Soo Eun Chang;Kirk I. Erickson;Nicoline G. Ambrose;Mark A. Hasegawa-Johnson

  • Singing-voice separation from monaural recordings using robust principal component analysis

    Po-Sen Huang;Scott Deeann Chen;Paris Smaragdis;Mark Hasegawa-Johnson

  • Dysarthric speech database for universal access research

    Heejin Kim;Mark Hasegawa-Johnson;Adrienne Perlman;Jon Gunderson

  • AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

    Kaizhi Qian;Yang Zhang;Shiyu Chang;Xuesong Yang

  • Dilated Recurrent Neural Networks

    Shiyu Chang;Yang Zhang;Wei Han;Mo Yu

  • AVICAR: audio-visual speech corpus in a car environment.

    Bowon Lee;Mark Hasegawa-Johnson;Camille Goudeseune;Suketu Kamdar

  • Signal-based and expectation-based factors in the perception of prosodic prominence

    Jennifer Cole;Yoonsook Mo;Mark Hasegawa-Johnson

  • Real-world acoustic event detection

    Xiaodan Zhuang;Xi Zhou;Mark A. Hasegawa-Johnson;Thomas S. Huang

  • Regression from patch-kernel

    Shuicheng Yan;Xi Zhou;Ming Liu;M. Hasegawa-Johnson

  • Acoustic fall detection using Gaussian mixture models and GMM supervectors

    Xiaodan Zhuang;Jing Huang;Gerasimos Potamianos;Mark Hasegawa-Johnson

  • Streaming Recommender Systems

    Shiyu Chang;Yang Zhang;Jiliang Tang;Dawei Yin

  • Prosodic effects on acoustic cues to stop voicing and place of articulation: Evidence from Radio News speech

    Jennifer Cole;Heejin Kim;Hansook Choi;Mark Hasegawa-Johnson

  • Singing-voice separation from monaural recordings using deep recurrent neural networks

    Po Sen Huang;Minje Kim;Mark Hasegawa-Johnson;Paris Smaragdis

  • Articulatory Feature-Based Methods for Acoustic and Audio-Visual Speech Recognition: Summary from the 2006 JHU Summer workshop

    K. Livescu;O. Cetin;M. Hasegawa-Johnson;S. King

  • SIFT-Bag kernel for video event analysis

    Xi Zhou;Xiaodan Zhuang;Shuicheng Yan;Shih-Fu Chang

  • Landmark-based speech recognition: report of the 2004 Johns Hopkins summer workshop

    M. Hasegawa-Johnson;J. Baker;S. Borys;K. Chen

  • Unsupervised Speech Decomposition via Triple Information Bottleneck

    Kaizhi Qian;Yang Zhang;Shiyu Chang;David Cox

  • Zero-Shot Voice Style Transfer with Only Autoencoder Loss.

    Kaizhi Qian;Yang Zhang;Shiyu Chang;Xuesong Yang

Frequent Co-Authors

Thomas S. Huang
Thomas S. Huang University of Illinois at Urbana-Champaign
Ken Chen
Ken Chen The University of Texas MD Anderson Cancer Center
Shiyu Chang
Shiyu Chang University of California, Santa Barbara
Stephen E. Levinson
Stephen E. Levinson University of Illinois at Urbana-Champaign
Deming Chen
Deming Chen University of Illinois at Urbana-Champaign
Daniel G. Morrow
Daniel G. Morrow University of Illinois at Urbana-Champaign
Najim Dehak
Najim Dehak Johns Hopkins University
Elliot Saltzman
Elliot Saltzman Boston University
Louis Goldstein
Louis Goldstein University of Southern California
Emmanuel Dupoux
Emmanuel Dupoux School for Advanced Studies in the Social Sciences

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