World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
7264
World Ranking
12915
National Ranking
1588

Nan Du 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 Nan Du 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: 131 publications — 19th percentile

19% of scientists in this discipline score the same or lower.

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

Nan Du 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 Nan Du 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Nan Du is a researcher affiliated with Tencent in China. Their work spans multiple domains primarily within computer science, with a significant focus on artificial intelligence. They have contributed extensively to various aspects of natural language processing and machine learning, with additional involvement in molecular biology and related fields.

The main fields of study for Nan Du include:

  • Computer Science

Within this main domain, their research encompasses several subfields such as:

  • Artificial Intelligence
  • Molecular Biology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Pulmonary and Respiratory Medicine

Nan Du's work touches on diverse topics, reflecting an interdisciplinary approach. Key research topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Ferroelectric and Negative Capacitance Devices
  • Speech and Dialogue Systems
  • RNA Modifications and Cancer

They have published in various academic venues, with the most frequent publication platforms being:

  • arXiv (Cornell University) - 31 publications
  • Journal of Experimental & Clinical Cancer Research - 2 publications
  • The Astrophysical Journal Supplement Series - 1 publication
  • The Science of The Total Environment - 1 publication
  • Molecular and Cellular Probes - 1 publication

Among Nan Du's recent publications are:

  • "PaLM: Scaling Language Modeling with Pathways," 2022, arXiv (Cornell University)
  • "ReAct: Synergizing Reasoning and Acting in Language Models," 2022, arXiv (Cornell University)
  • "GLaM: Efficient Scaling of Language Models with Mixture-of-Experts," 2021, arXiv (Cornell University)
  • "PaLM 2 Technical Report," 2023, arXiv (Cornell University)
  • "Helicobacter pylori-induced NAT10 stabilizes MDM2 mRNA via RNA acetylation to facilitate gastric cancer progression," 2023, Journal of Experimental & Clinical Cancer Research

Nan Du has worked collaboratively with several frequent co-authors, including:

  • Andrew M. Dai
  • Yanqi Zhou
  • Zhifeng Chen
  • Quoc V. Le
  • Pengyu Cheng

Best Publications

  • PaLM: Scaling Language Modeling with Pathways

    Unknown

  • Recurrent Marked Temporal Point Processes: Embedding Event History to Vector

    Nan Du;Hanjun Dai;Rakshit Trivedi;Utkarsh Upadhyay

  • PaLM 2 Technical Report

    Unknown

  • ReAct: Synergizing Reasoning and Acting in Language Models

    Unknown

  • AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy

    Wentao Zhu;Yufang Huang;Liang Zeng;Xuming Chen

  • Community detection in large-scale social networks

    Nan Du;Bin Wu;Xin Pei;Bai Wang

  • Scalable Influence Estimation in Continuous-Time Diffusion Networks

    Nan Du;Le Song;Manuel Gomez-Rodriguez;Hongyuan Zha

  • Influence Estimation and Maximization in Continuous-Time Diffusion Networks

    Manuel Gomez-Rodriguez;Le Song;Nan Du;Hongyuan Zha

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

    Xiaoyi Li;Nan Du;Hui Li;Kang Li

  • Joint Slot Filling and Intent Detection via Capsule Neural Networks

    Chenwei Zhang;Yaliang Li;Nan Du;Wei Fan

  • Learning Networks of Heterogeneous Influence

    Nan Du;Le Song;Ming Yuan;Alex J. Smola

  • Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams

    Nan Du;Mehrdad Farajtabar;Amr Ahmed;Alexander J. Smola

  • GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

    Unknown

  • Shaping Social Activity by Incentivizing Users

    Mehrdad Farajtabar;Nan Du;Manuel Gomez-Rodriguez;Isabel Valera

  • Time-sensitive recommendation from recurrent user activities

    Nan Du;Yichen Wang;Niao He;Le Song

  • Improved recommendation based on collaborative tagging behaviors

    Shiwan Zhao;Nan Du;Andreas Nauerz;Xiatian Zhang

  • Uncover Topic-Sensitive Information Diffusion Networks

    Nan Du;Le Song;Hyenkyun Woo;Hongyuan Zha

  • Constructing Disease Network and Temporal Progression Model via Context-Sensitive Hawkes Process

    Edward Choi;Nan Du;Robert Chen;Le Song

  • Influence Function Learning in Information Diffusion Networks

    Nan Du;Yingyu Liang;Maria Balcan;Le Song

  • A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network

    Nan Du;Bin Wu;Liutong Xu;Bai Wang

  • Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades

    Mehrdad Farajtabar;Manuel Gomez-Rodriguez;Nan Du;Mohammad Zamani

  • Finetuned Language Models Are Zero-Shot Learners

    Jason Wei;Maarten Bosma;Vincent Y. Zhao;Kelvin Guu

  • Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs

    Ying Shen;Yang Deng;Min Yang;Yaliang Li

  • Overlapping Community Detection in Bipartite Networks

    Nan Du;Bai Wang;Bin Wu;Yi Wang

  • Learning Temporal Point Processes via Reinforcement Learning

    Shuang Li;Shuai Xiao;Shixiang Zhu;Nan Du

  • MuVAN: A Multi-view Attention Network for Multivariate Temporal Data

    Ye Yuan;Guangxu Xun;Fenglong Ma;Yaqing Wang

  • Multi-Grained Named Entity Recognition

    Congying Xia;Chenwei Zhang;Tao Yang;Yaliang Li

  • Multi-grained Named Entity Recognition.

    Congying Xia;Chenwei Zhang;Tao Yang;Yaliang Li

  • Scalable Influence Estimation in Continuous-Time Diffusion Networks

    Nan Du;Le Song;Manuel Gomez Rodriguez;Hongyuan Zha

Frequent Co-Authors

Wei Fan
Wei Fan Tencent (China)
Yaliang Li
Yaliang Li Alibaba Group (China)
Aidong Zhang
Aidong Zhang University of Virginia
Le Song
Le Song Mohamed bin Zayed University of Artificial Intelligence
Jing Gao
Jing Gao Purdue University West Lafayette
Ying Shen
Ying Shen Sun Yat-sen University
Bin Wu
Bin Wu Beijing University of Posts and Telecommunications
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Kai Lei
Kai Lei Westlake University
Hongyuan Zha
Hongyuan Zha Chinese University of Hong Kong, Shenzhen

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