World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
17621
World Ranking
2456
National Ranking
1232

Jing Gao 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 Jing Gao 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: 213 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Jing Gao 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 Jing Gao 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: 65 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Overview

Jing Gao is affiliated with Purdue University West Lafayette in the United States and has an extensive research background primarily within the field of Computer Science, contributing to a total of 124 publications. The researcher's work spans various subfields with a focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Molecular Biology, and Information Systems.

The main topics of Jing Gao's research include:

  • Topic Modeling
  • Video Surveillance and Tracking Methods
  • Natural Language Processing Techniques
  • Advanced Image and Video Retrieval Techniques
  • Adversarial Robustness in Machine Learning
  • Energy Load and Power Forecasting
  • Text and Document Classification Technologies

Among Jing Gao's recent publications are:

  • A Survey on Deep Learning for Multimodal Data Fusion, 2020, Neural Computation
  • A Survey on Causal Inference, 2021, ACM Transactions on Knowledge Discovery from Data
  • Weak Supervision for Fake News Detection via Reinforcement Learning, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • SmallTrack: Wavelet Pooling and Graph Enhanced Classification for UAV Small Object Tracking, 2023, IEEE Transactions on Geoscience and Remote Sensing
  • Adaptive Self-training for Few-shot Neural Sequence Labeling, 2020, arXiv (Cornell University)

Jing Gao frequently publishes in several venues. The main publication venues include:

  • arXiv (Cornell University)
  • Journal of Ultrasound in Medicine
  • SSRN Electronic Journal
  • Journal of Physics Conference Series
  • Proceedings of the AAAI Conference on Artificial Intelligence

The researcher has collaborated often with several co-authors, among whom the most frequent are:

  • Zhikui Chen
  • Peng Li
  • Samer Narouze
  • J. Brian Fowlkes
  • Stamatia Destounis

Best Publications

  • Outlier Detection for Temporal Data: A Survey

    Manish Gupta;Jing Gao;Charu C. Aggarwal;Jiawei Han

  • EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection

    Yaqing Wang;Fenglong Ma;Zhiwei Jin;Ye Yuan

  • Multi-view clustering via joint nonnegative matrix factorization

    Jing Gao;Jiawei Han;Jialu Liu;Chi Wang

  • Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation

    Qi Li;Yaliang Li;Jing Gao;Bo Zhao

  • Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

    Fenglong Ma;Radha Chitta;Jing Zhou;Quanzeng You

  • Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

    Mohammad M Masud;Jing Gao;L Khan;Jiawei Han

  • A Survey on Truth Discovery

    Yaliang Li;Jing Gao;Chuishi Meng;Qi Li

  • Knowledge transfer via multiple model local structure mapping

    Jing Gao;Wei Fan;Jing Jiang;Jiawei Han

  • A Survey on Causal Inference

    Liuyi Yao;Zhixuan Chu;Sheng Li;Yaliang Li

  • A confidence-aware approach for truth discovery on long-tail data

    Qi Li;Yaliang Li;Jing Gao;Lu Su

  • Multistep-ahead time series prediction

    Haibin Cheng;Pang-Ning Tan;Jing Gao;Jerry Scripps

  • A deep learning approach for detecting traffic accidents from social media data

    Zhenhua Zhang;Qing He;Jing Gao;Ming Ni

  • On community outliers and their efficient detection in information networks

    Jing Gao;Feng Liang;Wei Fan;Chi Wang

  • A general framework for mining concept-drifting data streams with skewed distributions

    Jing Gao;Wei Fan;Jiawei Han;Philip S. Yu

  • Graph regularized transductive classification on heterogeneous information networks

    Ming Ji;Yizhou Sun;Marina Danilevsky;Jiawei Han

  • A Deep Learning Approach to Link Prediction in Dynamic Networks.

    Xiaoyi Li;Nan Du;Hui Li;Kang Li

  • FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation

    Fenglong Ma;Yaliang Li;Qi Li;Minghui Qiu

  • Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media

    Ming Ni;Qing He;Jing Gao

  • On Appropriate Assumptions to Mine Data Streams: Analysis and Practice

    Jing Gao;Wei Fan;Jiawei Han

  • Outlier Detection for Temporal Data

    Manish Gupta;Jing Gao;Charu Aggarwal;Jiawei Han

  • Representation Learning for Treatment Effect Estimation from Observational Data

    Liuyi Yao;Sheng Li;Yaliang Li;Mengdi Huai

Frequent Co-Authors

Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Aidong Zhang
Aidong Zhang University of Virginia
Fenglong Ma
Fenglong Ma Pennsylvania State University
Yaliang Li
Yaliang Li Alibaba Group (China)
Lu Su
Lu Su Purdue University West Lafayette
Wei Fan
Wei Fan Tencent (China)
Nan Du
Nan Du Tencent (China)
Latifur Khan
Latifur Khan The University of Texas at Dallas
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Bhavani Thuraisingham
Bhavani Thuraisingham The University of Texas at Dallas

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