World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
96
Citations
45231
World Ranking
430
National Ranking
238

ChengXiang Zhai 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 ChengXiang Zhai 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: 483 publications — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

ChengXiang Zhai 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 ChengXiang Zhai 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: 96 D-Index — 97th percentile

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

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

Research.com Recognitions

  • 2017 - ACM Fellow For contributions to information retrieval and text data mining
  • 2009 - ACM Distinguished Member
  • 2008 - Fellow of Alfred P. Sloan Foundation

Overview

ChengXiang Zhai is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence, supported by contributions in Information Systems, Molecular Biology, Computer Vision and Pattern Recognition, as well as Sociology and Political Science.

The scientist's work covers a range of topics, notably:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Biomedical Text Mining and Ontologies
  • Misinformation and Its Impacts
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Recommender Systems and Techniques

Zhai has published numerous papers in a variety of venues, reflecting interdisciplinary interests. Frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Knowledge and Data Engineering
  • 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
  • IET conference proceedings.

Recent notable papers authored or co-authored by them are:

  • "AutoML to Date and Beyond: Challenges and Opportunities" (2021), published in ACM Computing Surveys
  • "Biosystems Design by Machine Learning" (2020), published in ACS Synthetic Biology
  • "Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries" (2021), published in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "KEBLM: Knowledge-Enhanced Biomedical Language Models" (2023), published in Journal of Biomedical Informatics
  • "Transductive Ensemble Learning for Neural Machine Translation" (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence

They have collaborated frequently with several other researchers, including:

  • Heng Ji
  • Ismini Lourentzou
  • Alex Morales
  • Krisztian Balog
  • Daniel Campos

Among the awards received, ChengXiang Zhai was recognized as an ACM Fellow in 2017 for contributions to information retrieval and text data mining. Other distinctions include being named an ACM Distinguished Member in 2009 and a Fellow of the Alfred P. Sloan Foundation in 2008.

Best Publications

  • A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval

    Chengxiang Zhai;John Lafferty

  • Mining Text Data

    Charu C. Aggarwal;Cheng Xiang Zhai

  • Big data: Astronomical or genomical?

    Zachary D. Stephens;Skylar Y. Lee;Faraz Faghri;Roy H. Campbell

  • A study of smoothing methods for language models applied to information retrieval

    Chengxiang Zhai;John Lafferty

  • A study of smoothing methods for language models applied to Ad Hoc information retrieval

    Unknown

  • A survey of text classification algorithms

    Charu C. Aggarwal;Cheng Xiang Zhai

  • Document Language Models, Query Models, and Risk Minimization for Information Retrieval

    John Lafferty;Chengxiang Zhai

  • Topic sentiment mixture: modeling facets and opinions in weblogs

    Qiaozhu Mei;Xu Ling;Matthew Wondra;Hang Su

  • Instance Weighting for Domain Adaptation in NLP

    Jing Jiang;ChengXiang Zhai

  • Model-based feedback in the language modeling approach to information retrieval

    Chengxiang Zhai;John Lafferty

  • A Survey of Text Clustering Algorithms

    Charu C. Aggarwal;Cheng Xiang Zhai

  • Latent aspect rating analysis on review text data: a rating regression approach

    Hongning Wang;Yue Lu;Chengxiang Zhai

  • Discovering evolutionary theme patterns from text: an exploration of temporal text mining

    Qiaozhu Mei;ChengXiang Zhai

  • Context-sensitive information retrieval using implicit feedback

    Xuehua Shen;Bin Tan;ChengXiang Zhai

  • Implicit user modeling for personalized search

    Xuehua Shen;Bin Tan;ChengXiang Zhai

  • Statistical Language Models for Information Retrieval

    ChengXiang Zhai

  • Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions

    Kavita Ganesan;ChengXiang Zhai;Jiawei Han

  • Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval

    Cheng Xiang Zhai;William W. Cohen;John Lafferty

  • Automatic labeling of multinomial topic models

    Qiaozhu Mei;Xuehua Shen;ChengXiang Zhai

  • Topic modeling with network regularization

    Qiaozhu Mei;Deng Cai;Duo Zhang;ChengXiang Zhai

  • Rated aspect summarization of short comments

    Yue Lu;ChengXiang Zhai;Neel Sundaresan

  • Document language models, query models, and risk minimization for information retrieval

    Unknown

Frequent Co-Authors

Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Qiaozhu Mei
Qiaozhu Mei University of Michigan–Ann Arbor
Jing Jiang
Jing Jiang Singapore Management University
Xuanhui Wang
Xuanhui Wang Google (United States)
John Lafferty
John Lafferty Yale University
Tie-Yan Liu
Tie-Yan Liu Microsoft (United States)
Dan Roth
Dan Roth University of Pennsylvania
Yi Chang
Yi Chang Jilin University
James Allan
James Allan University of Massachusetts Amherst
Dolores Albarracín
Dolores Albarracín University of Pennsylvania

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