World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
5964
World Ranking
12056
National Ranking
154

Chang D. Yoo 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 Chang D. Yoo 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: 201 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Chang D. Yoo 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 Chang D. Yoo 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Chang D. Yoo is affiliated with the Korea Advanced Institute of Science and Technology in South Korea and has contributed extensively to the field of computer science. Their research spans several specialized subfields, including computer vision and pattern recognition, artificial intelligence, signal processing, biomedical engineering, and aerospace engineering.

The scientist's work focuses on a range of topics within these domains. Notable areas of study include:

  • Multimodal machine learning applications
  • Domain adaptation and few-shot learning
  • Human pose and action recognition
  • Topic modeling
  • Speech recognition and synthesis
  • Video analysis and summarization
  • Adversarial robustness in machine learning

Their publication record features a significant number of articles in prominent venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • IEEE Access
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Sensors
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Chang D. Yoo has collaborated regularly with several coauthors, including:

  • Sunjae Yoon
  • Tung M. Luu
  • Trung X. Pham
  • Mark Hasegawa-Johnson
  • Ji Woo Hong

Selected recent papers highlight the range and impact of their research:

  • "SoftGroup for 3D Instance Segmentation on Point Clouds," 2022, published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Biomimetic and flexible piezoelectric mobile acoustic sensors with multiresonant ultrathin structures for machine learning biometrics," 2021, Science Advances
  • "Clinical Validation of a Wearable Piezoelectric Blood-Pressure Sensor for Continuous Health Monitoring," 2023, Advanced Materials
  • "Semantic Grouping Network for Video Captioning," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "SCNet: Training Inference Sample Consistency for Instance Segmentation," 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Best Publications

  • Edge-Labeling Graph Neural Network for Few-Shot Learning

    Jongmin Kim;Taesup Kim;Sungwoong Kim;Chang D. Yoo

  • Reversible Image Watermarking Based on Integer-to-Integer Wavelet Transform

    Sunil Lee;C.D. Yoo;T. Kalker

  • Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing.

    Young Hoon Jung;Seong Kwang Hong;Hee Seung Wang;Jae Hyun Han

  • Robust Video Fingerprinting for Content-Based Video Identification

    Sunil Lee;C.D. Yoo

  • A robust image fingerprinting system using the Radon transform

    Jin S. Seo;Jaap Haitsma;Ton Kalker;Chang Dong Yoo

  • Image watermarking based on invariant regions of scale-space representation

    J.S. Seo;C.D. Yoo

  • Biomimetic and flexible piezoelectric mobile acoustic sensors with multiresonant ultrathin structures for machine learning biometrics.

    Hee Seung Wang;Seong Kwang Hong;Jae Hyun Han;Young Hoon Jung

  • Machine learning-based self-powered acoustic sensor for speaker recognition

    Jae Hyun Han;Kang Min Bae;Seong Kwang Hong;Hyunsin Park

  • Higher-Order Correlation Clustering for Image Segmentation

    Sungwoong Kim;Sebastian Nowozin;Pushmeet Kohli;Chang D. Yoo

  • Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks

    Sanghyuk Park;Fei Pan;Sunghun Kang;Chang Dong Yoo

  • Underdetermined Blind Source Separation Based on Subspace Representation

    SangGyun Kim;C.D. Yoo

  • Face alignment using cascade Gaussian process regression trees

    Donghoon Lee;Hyunsin Park;Chang D. Yoo

  • Semantic Grouping Network for Video Captioning.

    Hobin Ryu;Sunghun Kang;Haeyong Kang;Chang D. Yoo

  • Image Segmentation Using Higher-Order Correlation Clustering.

    Sungwoong Kim;Chang Dong Yoo;Sebastian Nowozin;Pushmeet Kohli

  • Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution

    Thang Vu;Hyunjun Jang;Trung X. Pham;Chang Dong Yoo

  • Audio fingerprinting based on normalized spectral subband moments

    J.S. Seo;Minho Jin;Sunil Lee;Dalwon Jang

  • Audio fingerprinting based on normalized spectral subband centroids

    J.S. Seo;Minho Jin;Sunil Lee;Dalwon Jang

  • Localized image watermarking based on feature points of scale-space representation

    Jin S. Seo;Chang Dong Yoo

  • SCNet: Training Inference Sample Consistency for Instance Segmentation

    Thang Vu;Haeyong Kang;Chang D. Yoo

  • Progressive Attention Memory Network for Movie Story Question Answering

    Junyeong Kim;Minuk Ma;Kyungsu Kim;Sungjin Kim

  • Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks.

    Thang Vu;Cao Van Nguyen;Trung X. Pham;Tung Minh Luu

  • Underdetermined Blind Source Separation Based on

    SangGyun Kim;Chang D. Yoo

Frequent Co-Authors

Keon Jae Lee
Keon Jae Lee Korea Advanced Institute of Science and Technology
Ton Kalker
Ton Kalker Hewlett-Packard (United States)
Jinho Choi
Jinho Choi University of Adelaide
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Mark Hasegawa-Johnson
Mark Hasegawa-Johnson University of Illinois at Urbana-Champaign
Pascal Fua
Pascal Fua École Polytechnique Fédérale de Lausanne
Arnaud Doucet
Arnaud Doucet University of Oxford
Chang Kyu Jeong
Chang Kyu Jeong Jeonbuk National University

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