World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
14079
World Ranking
3419
National Ranking
457

Hua Wu 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 Hua Wu 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: 239 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Hua Wu 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 Hua Wu 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: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Overview

Hua Wu is affiliated with Baidu (China) in China and works primarily in the field of Computer Science, with a significant focus on Artificial Intelligence. Their research portfolio includes contributions to Computer Vision and Pattern Recognition, Molecular Biology, Computational Theory and Mathematics, and Materials Chemistry.

The scientist's work covers a range of topics including Topic Modeling, Natural Language Processing Techniques, Multimodal Machine Learning Applications, Computational Drug Discovery Methods, Speech and Dialogue Systems, Machine Learning in Materials Science, and Text and Document Classification Technologies.

Notable recent publications by Hua Wu include:

  • ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Geometry-enhanced molecular representation learning for property prediction, 2022, Nature Machine Intelligence
  • Unified Structure Generation for Universal Information Extraction, 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Pre-Trained Language Models and Their Applications, 2022, Engineering
  • ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs, 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors collaborating with Hua Wu include:

  • Haifeng Wang
  • Xinyan Xiao
  • Hao Tian
  • Xiaomin Fang
  • Shuohuan Wang

Common venues where Hua Wu's research has been published are:

  • arXiv (Cornell University)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Engineering

Best Publications

  • ERNIE: Enhanced Representation through Knowledge Integration

    Yu Sun;Shuohuan Wang;Yukun Li;Shikun Feng

  • ERNIE 2.0: A Continual Pre-training Framework for Language Understanding

    Yu Sun;Shuohuan Wang;Yukun Li;Shikun Feng

  • Multi-Task Learning for Multiple Language Translation

    Daxiang Dong;Hua Wu;Wei He;Dianhai Yu

  • Minimum Risk Training for Neural Machine Translation

    Shiqi Shen;Yong Cheng;Zhongjun He;Wei He

  • RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

    Yingqi Qu;Yuchen Ding;Jing Liu;Kai Liu

  • An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge

    Yanchao Hao;Yuanzhe Zhang;Kang Liu;Shizhu He

  • Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

    Rui Yan;Yiping Song;Hua Wu

  • Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network

    Xiangyang Zhou;Lu Li;Daxiang Dong;Yi Liu

  • Pre-Trained Language Models and Their Applications

    Unknown

  • Pivot Language Approach for Phrase-Based Statistical Machine Translation

    Hua Wu;Haifeng Wang

  • SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

    Hao Tian;Can Gao;Xinyan Xiao;Hao Liu

  • PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable

    Siqi Bao;Huang He;Fan Wang;Hua Wu

  • UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning

    Wei Li;Can Gao;Guocheng Niu;Xinyan Xiao

  • Multi-view Response Selection for Human-Computer Conversation

    Xiangyang Zhou;Daxiang Dong;Hua Wu;Shiqi Zhao

  • DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications

    Wei He;Kai Liu;Jing Liu;Yajuan Lyu

  • Semi-Supervised Learning for Neural Machine Translation

    Yong Cheng;Wei Xu;Zhongjun He;Wei He

  • STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework

    Mingbo Ma;Liang Huang;Hao Xiong;Renjie Zheng

  • Learning to Select Knowledge for Response Generation in Dialog Systems

    Rongzhong Lian;Min Xie;Fan Wang;Jinhua Peng

  • ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs.

    Fei Yu;Jiji Tang;Weichong Yin;Yu Sun

  • ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

    Yu Sun;Shuohuan Wang;Shikun Feng;Siyu Ding

  • Towards Conversational Recommendation over Multi-Type Dialogs

    Zeming Liu;Haifeng Wang;Zheng-Yu Niu;Hua Wu

  • ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph

    Fei Yu;Jiji Tang;Weichong Yin;Yu Sun

  • Proactive Human-Machine Conversation with Explicit Conversation Goals

    Wenquan Wu;Zhen Guo;Xiangyang Zhou;Hua Wu

Frequent Co-Authors

Haifeng Wang
Haifeng Wang Baidu (China)
Wanxiang Che
Wanxiang Che Harbin Institute of Technology
Ming Zhou
Ming Zhou Langboat Technology
Wayne Xin Zhao
Wayne Xin Zhao Renmin University of China
Ting Liu
Ting Liu Harbin Institute of Technology
Maosong Sun
Maosong Sun Tsinghua University
Yang Liu
Yang Liu Tsinghua University
Chengqing Zong
Chengqing Zong Chinese Academy of Sciences
Liang Huang
Liang Huang Oregon State University
Sujian Li
Sujian Li Peking University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

If you’re interested in computer science, you’ll find many other closely related online degrees and evolving career pathways. For those drawn to applied sciences, an online degree in mechanical engineering offers foundational knowledge that overlaps with robotics and automation in computer science.

For students who love the fundamentals, a bachelor of science in physics online can lead to computational physics or software engineering roles, providing strong analytical and problem-solving skills.

Data-driven careers are also booming. Explore data science programs to develop expertise in big data, analytics, and AI, all high-demand fields for computer science graduates.

Additionally, software and hardware often intersect. Completing online electrical engineering courses USA can prepare you for careers in embedded systems, IoT, and digital hardware, complementing your computer science education.

These online degree pathways can open doors in both research and industry, giving you the flexibility to shape your future in tech.

Best Scientists Citing Hua Wu

Trending Scientists

Recently Published Articles