World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
6403
World Ranking
11148
National Ranking
133

Jaegul Choo 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 Jaegul Choo 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: 331 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Jaegul Choo 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 Jaegul Choo 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

Jaegul Choo is affiliated with the Korea Advanced Institute of Science and Technology in South Korea. Their research primarily focuses on the field of Computer Science with a significant output in subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Computer Graphics and Computer-Aided Design, and Biomedical Engineering.

Their work engages with a variety of topics including Generative Adversarial Networks and Image Synthesis, Advanced Vision and Imaging, Topic Modeling, Natural Language Processing Techniques, Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, and Computer Graphics and Visualization Techniques.

Jaegul Choo's recent publications include the following papers:

  • Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks, 2020, Neural Networks
  • Prediction of hand-wrist maturation stages based on cervical vertebrae images using artificial intelligence, 2021, Orthodontics and Craniofacial Research
  • Project Recommendation Using Heterogeneous Traits in Crowdfunding, 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • BiaSwap: Removing Dataset Bias with Bias-Tailored Swapping Augmentation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Learning Debiased Representation via Disentangled Feature Augmentation, 2021, arXiv (Cornell University)

Frequent co-authors collaborating with Jaegul Choo include Edward Choi, Sunghyun Park, Jooyeol Yun, Jungsoo Lee, and Junha Hyung.

The most common publication venues for their work are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • IEEE Transactions on Visualization and Computer Graphics

Best Publications

  • StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation

    Yunjey Choi;Minje Choi;Munyoung Kim;Jung-Woo Ha

  • A Multi-Organ Nucleus Segmentation Challenge

    Neeraj Kumar;Ruchika Verma;Deepak Anand;Yanning Zhou

  • UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization

    Jaegul Choo;Changhyun Lee;Chandan K. Reddy;Haesun Park

  • RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records

    Bum Chul Kwon;Min-Je Choi;Joanne Taery Kim;Edward Choi

  • Visual Analytics for Explainable Deep Learning

    Jaegul Choo;Shixia Liu

  • iVisClustering: An Interactive Visual Document Clustering via Topic Modeling

    Hanseung Lee;Jaeyeon Kihm;Jaegul Choo;John Stasko

  • RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

    Sungha Choi;Sanghun Jung;Huiwon Yun;Joanne T. Kim

  • Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations

    Tian Shi;Kyeongpil Kang;Jaegul Choo;Chandan K. Reddy

  • iVisClassifier: An interactive visual analytics system for classification based on supervised dimension reduction

    Jaegul Choo;Hanseung Lee;Jaeyeon Kihm;Haesun Park

  • Nonnegative Matrix Factorization for Interactive Topic Modeling and Document Clustering

    D. da Kuang;Jaegul Choo;Haesun Park

  • Cars Can’t Fly Up in the Sky: Improving Urban-Scene Segmentation via Height-Driven Attention Networks

    Sungha Choi;Joanne T. Kim;Jaegul Choo

  • StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

    Yunjey Choi;Minje Choi;Munyoung Kim;Jung-Woo Ha

  • ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed

    Cheonbok Park;Chunggi Lee;Hyojin Bahng;Yunwon Tae

  • Reference-Based Sketch Image Colorization Using Augmented-Self Reference and Dense Semantic Correspondence

    Junsoo Lee;Eungyeup Kim;Yunsung Lee;Dongjun Kim

  • Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization

    Jinho Choi;Sanghun Jung;Deok Gun Park;Jaegul Choo

  • Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks

    Seungjoo Yoo;Hyojin Bahng;Sunghyo Chung;Junsoo Lee

  • Combining Computational Analyses and Interactive Visualization for Document Exploration and Sensemaking in Jigsaw

    C. Gorg;Zhicheng Liu;Jaeyeon Kihm;Jaegul Choo

  • Image-To-Image Translation via Group-Wise Deep Whitening-And-Coloring Transformation

    Wonwoong Cho;Sungha Choi;David Keetae Park;Inkyu Shin

  • VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization

    Seunghwan Choi;Sunghyun Park;Minsoo Lee;Jaegul Choo

  • Learning De-biased Representations with Biased Representations

    Hyojin Bahng;Sanghyuk Chun;Sangdoo Yun;Jaegul Choo

  • InterAxis: Steering Scatterplot Axes via Observation-Level Interaction

    Hannah Kim;Jaegul Choo;Haesun Park;Alex Endert

  • High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions

    Unknown

  • When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation.

    Young Bin Kim;Jurim Lee;Nuri Park;Jaegul Choo

  • Personas in online health communities

    Jina Huh;Bum Chul Kwon;Sung-Hee Kim;Sukwon Lee

  • Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation

    Hyojin Bahng;Seungjoo Yoo;Wonwoong Cho;David Keetae Park;David Keetae Park

  • TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections

    Minjeong Kim;Kyeongpil Kang;Deokgun Park;Jaegul Choo

  • ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding

    Deokgun Park;Seungyeon Kim;Jurim Lee;Jaegul Choo

Frequent Co-Authors

Haesun Park
Haesun Park Georgia Institute of Technology
Chandan K. Reddy
Chandan K. Reddy Virginia Tech
John Stasko
John Stasko Georgia Institute of Technology
Jung-Woo Ha
Jung-Woo Ha Naver (South Korea)
Sunghun Kim
Sunghun Kim Hong Kong University of Science and Technology
Duen Horng Chau
Duen Horng Chau Georgia Institute of Technology
Hongyuan Zha
Hongyuan Zha Chinese University of Hong Kong, Shenzhen
Niklas Elmqvist
Niklas Elmqvist University of Maryland, College Park
Alex Endert
Alex Endert Georgia Institute of Technology
Tao Qin
Tao Qin Microsoft (United States)

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