World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
31147
World Ranking
2517
National Ranking
1256

Xiaolong 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 Xiaolong 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: 613 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Xiaolong 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 Xiaolong 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: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Overview

Xiaolong Wang is affiliated with the University of California, San Diego, in the United States. Their research primarily spans the fields of Engineering and Materials Science, with significant contributions in related subfields such as Biomedical Engineering, Mechanical Engineering, Materials Chemistry, Electrical and Electronic Engineering, and Automotive Engineering.

Their scholarly output includes a focus on several key topics including Additive Manufacturing and 3D Printing Technologies, Advanced Sensor and Energy Harvesting Materials, Advanced Materials and Mechanics, Catalytic Processes in Materials Science, Surface Modification and Superhydrophobicity, 3D Printing in Biomedical Research, and Adhesion, Friction, and Surface Interactions.

Wang has frequently published in a range of scientific journals. The venues where they have the most publications include:

  • Chemical Engineering Journal
  • Tribology International
  • Advanced Functional Materials
  • Small
  • SSRN Electronic Journal

Their collaborative network features several frequent coauthors with whom they have published multiple times. Notable collaborators include Zhongying Ji, Pan Jiang, Desheng Liu, Xin Jia, and Yaozhong Lu.

Recent papers authored or coauthored by Xiaolong Wang illustrate the breadth of their research interests and engagement with advanced material sciences and engineering challenges. Selected publications include:

  • "Tough, Transparent, and Slippery PVA Hydrogel Led by Syneresis," 2023, Small
  • "3D Printing of Dual-Physical Cross-linking Hydrogel with Ultrahigh Strength and Toughness," 2020, Chemistry of Materials
  • "3D printing of metal-organic frameworks decorated hierarchical porous ceramics for high-efficiency catalytic degradation," 2020, Chemical Engineering Journal
  • "Strong and Ultra-Tough Supramolecular Hydrogel Enabled by Strain-Induced Microphase Separation," 2022, Advanced Functional Materials
  • "High-performance Cu/ZnO/Al2O3 catalysts for methanol steam reforming with enhanced Cu-ZnO synergy effect via magnesium assisted strategy," 2021, Journal of Energy Chemistry

The range of these publications highlights their involvement in topics including hydrogels with novel mechanical properties, 3D printing applications in material science, catalytic processes, and supramolecular hydrogel technology.

Best Publications

  • Non-local Neural Networks

    Xiaolong Wang;Ross Girshick;Abhinav Gupta;Kaiming He

  • Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding

    Gunnar A. Sigurdsson;Gül Varol;Xiaolong Wang;Ali Farhadi;Ali Farhadi

  • Unsupervised Learning of Visual Representations Using Videos

    Xiaolong Wang;Abhinav Gupta

  • Videos as Space-Time Region Graphs

    Xiaolong Wang;Abhinav Gupta

  • A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

    Xiaolong Wang;Abhinav Shrivastava;Abhinav Gupta

  • Learning Continuous Image Representation with Local Implicit Image Function

    Yinbo Chen;Sifei Liu;Xiaolong Wang

  • Generative Image Modeling Using Style and Structure Adversarial Networks

    Xiaolong Wang;Abhinav Gupta

  • Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs

    Xiaolong Wang;Yufei Ye;Abhinav Gupta

  • Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

    Yinbo Chen;Zhuang Liu;Huijuan Xu;Trevor Darrell

  • 3D Human Pose Estimation in the Wild by Adversarial Learning

    Wei Yang;Wanli Ouyang;Xiaolong Wang;Jimmy Ren

  • Designing deep networks for surface normal estimation

    Xiaolong Wang;David F. Fouhey;Abhinav Gupta

  • Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory

    Xin Wang;Yuanchao Liu;Chengjie Sun;Baoxun Wang

  • Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention

    Yang Liu;Chengjie Sun;Lei Lin;Xiaolong Wang

  • Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

    Yu Sun;Xiaolong Wang;Zhuang Liu;John Miller

  • Deeply-Learned Feature for Age Estimation

    Xiaolong Wang;Rui Guo;Chandra Kambhamettu

  • Efficient generation of mouse models with the prime editing system.

    Yao Liu;Xiangyang Li;Siting He;Shuhong Huang

  • Visual Semantic Navigation using Scene Priors

    Wei Yang;Xiaolong Wang;Ali Farhadi;Abhinav Gupta

  • Transitive Invariance for Self-Supervised Visual Representation Learning

    Xiaolong Wang;Kaiming He;Abhinav Gupta

  • Entity recognition from clinical texts via recurrent neural network

    Zengjian Liu;Ming Yang;Xiaolong Wang;Qingcai Chen

  • Active deep learning method for semi-supervised sentiment classification

    Shusen Zhou;Qingcai Chen;Xiaolong Wang

  • A New Meta-Baseline for Few-Shot Learning

    Yinbo Chen;Xiaolong Wang;Zhuang Liu;Huijuan Xu

Frequent Co-Authors

Qingcai Chen
Qingcai Chen Harbin Institute of Technology
Feng Zhou
Feng Zhou Lanzhou Institute of Chemical Physics
Buzhou Tang
Buzhou Tang Harbin Institute of Technology
Abhinav Gupta
Abhinav Gupta Carnegie Mellon University
Chandra Kambhamettu
Chandra Kambhamettu University of Delaware
Weimin Liu
Weimin Liu Chinese Academy of Sciences
Bo Yu
Bo Yu Chinese Academy of Sciences
Ruifeng Xu
Ruifeng Xu Harbin Institute of Technology
Xingxu Huang
Xingxu Huang First Affiliated Hospital Zhejiang University
Yongjiu Cai
Yongjiu Cai Chinese Academy of Sciences

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