World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
3510
World Ranking
13745
National Ranking
5459

Chaoli Wang 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 Chaoli Wang 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: 139 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Chaoli Wang 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 Chaoli Wang 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: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Overview

Chaoli Wang is affiliated with the University of Notre Dame in the United States. Their research primarily centers on computer science, with a strong focus on computer vision and pattern recognition, artificial intelligence, and computer graphics and computer-aided design. Additional subfields include statistical and nonlinear physics as well as radiology, nuclear medicine, and imaging.

The scientist's work spans a variety of topics, notably data visualization and analytics, advanced vision and imaging, computer graphics and visualization techniques, advanced image processing techniques, image and signal denoising methods, advanced neural network applications, and generative adversarial networks and image synthesis.

Chaoli Wang has published extensively in several venues. Frequent publication outlets include:

  • IEEE Transactions on Visualization and Computer Graphics
  • arXiv (Cornell University)
  • Computers & Graphics
  • Visual Informatics
  • IEEE Computer Graphics and Applications

Recent papers by or involving Chaoli Wang include:

  • DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization, 2022, IEEE Transactions on Visualization and Computer Graphics
  • An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training, 2020, Proceedings of the AAAI Conference on Artificial Intelligence

Other notable recent works, authored by frequent collaborators, reflect the scientist's involvement in time-varying data analysis and visualization:

  • SSR-TVD: Spatial Super-Resolution for Time-Varying Data Analysis and Visualization, 2020, IEEE Transactions on Visualization and Computer Graphics
  • CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network, 2022, IEEE Transactions on Visualization and Computer Graphics
  • V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data, 2020, IEEE Transactions on Visualization and Computer Graphics

Frequent co-authors in their network include Jun Han, Danny Z. Chen, Siyuan Yao, Siavash Ghorbany, and Ming Hu, indicating a collaborative research environment and cross-disciplinary engagement.

Best Publications

  • In Situ Visualization for Large-Scale Combustion Simulations

    Hongfeng Yu;Chaoli Wang;Ray W Grout;Jacqueline H Chen

  • Importance-Driven Time-Varying Data Visualization

    Chaoli Wang;Hongfeng Yu;Kwan-Liu Ma

  • Information Theory in Scientific Visualization

    Chaoli Wang;Han-Wei Shen

  • Massively parallel volume rendering using 2-3 swap image compositing

    Hongfeng Yu;Chaoli Wang;Kwan-Liu Ma

  • High dimensional direct rendering of time-varying volumetric data

    J. Woodring;Chaoli Wang;Han-Wei Shen

  • In-situ processing and visualization for ultrascale simulations

    Kwan-Liu Ma;Chaoli Wang;Hongfeng Yu;Anna Tikhonova

  • Parallel hierarchical visualization of large time-varying 3D vector fields

    Hongfeng Yu;Chaoli Wang;Kwan-Liu Ma

  • Hierarchical Streamline Bundles

    Hongfeng Yu;Chaoli Wang;Ching-Kuang Shene;J. H. Chen

  • A Unified Approach to Streamline Selection and Viewpoint Selection for 3D Flow Visualization

    Jun Tao;Jun Ma;Chaoli Wang;Ching-Kuang Shene

  • FlowNet: A Deep Learning Framework for Clustering and Selection of Streamlines and Stream Surfaces

    Jun Han;Jun Tao;Chaoli Wang

  • A New Ensemble Learning Framework for 3D Biomedical Image Segmentation.

    Hao Zheng;Yizhe Zhang;Lin Yang;Peixian Liang

  • TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data

    Yi Gu;Chaoli Wang

  • Feature-Preserving Volume Data Reduction and Focus+Context Visualization

    Yu-Shuen Wang;Chaoli Wang;Tong-Yee Lee;Kwan-Liu Ma

  • A multiresolution volume rendering framework for large-scale time-varying data visualization

    Chaoli Wang;Jinzhu Gao;Liya Li;Han-Wei Shen

  • Massively parallel volume rendering using 2-3 swap image compositing

    Hongfeng Yu;Chaoli Wang;Kwan Liu Ma

  • Correlation study of time-varying multivariate climate data sets

    Jeffrey Sukharev;Chaoli Wang;Kwan-Liu Ma;Andrew T. Wittenberg

  • Application-Driven Compression for Visualizing Large-Scale Time-Varying Data

    Chaoli Wang;Hongfeng Yu;Kwan-Liu Ma

  • TSR-TVD: Temporal Super-Resolution for Time-Varying Data Analysis and Visualization

    Jun Han;Chaoli Wang

  • Interactive Level-of-Detail Selection Using Image-Based Quality Metric for Large Volume Visualization

    C. Wang;A. Garcia;H.-W. Shen

  • LOD Map - A Visual Interface for Navigating Multiresolution Volume Visualization

    C. Wang;H.-W. Shen

  • Parallel multiresolution volume rendering of large data sets with error-guided load balancing

    Chaoli Wang;Jinzhu Gao;Han-Wei Shen

  • Biomedical Image Segmentation via Representative Annotation

    Hao Zheng;Lin Yang;Jianxu Chen;Jun Han

  • SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization

    Li Guo;Shaojie Ye;Jun Han;Hao Zheng

  • A sketch-based interface for classifying and visualizing vector fields

    Jishang Wei;Chaoli Wang;Hongfeng Yu;Kwan-Liu Ma

Frequent Co-Authors

Kwan-Liu Ma
Kwan-Liu Ma University of California, Davis
Danny Z. Chen
Danny Z. Chen University of Notre Dame
Han-Wei Shen
Han-Wei Shen The Ohio State University
Nitesh V. Chawla
Nitesh V. Chawla University of Notre Dame
Jacqueline H. Chen
Jacqueline H. Chen Sandia National Laboratories
Hanghang Tong
Hanghang Tong University of Illinois at Urbana-Champaign
Tong-Yee Lee
Tong-Yee Lee National Cheng Kung University
Andrew T. Wittenberg
Andrew T. Wittenberg Geophysical Fluid Dynamics Laboratory
Sidney K. D'Mello
Sidney K. D'Mello University of Colorado Boulder
Gordon G. Parker
Gordon G. Parker Michigan Technological University

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