World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
71
Citations
28088
World Ranking
1740
National Ranking
886

Xiaojin Zhu 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 Xiaojin Zhu 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: 189 publications — 42nd percentile

42% of scientists in this discipline score the same or lower.

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

Xiaojin Zhu 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 Xiaojin Zhu 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: 71 D-Index — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Overview

Xiaojin Zhu is affiliated with the University of Wisconsin-Madison in the United States. Their work intersects the fields of Computer Science and Engineering, with a focus spanning several specialized subfields including Artificial Intelligence, Control and Systems Engineering, Clinical Psychology, Management Science and Operations Research, and Computer Vision and Pattern Recognition.

The scientist's research topics cover areas such as Reinforcement Learning in Robotics, Adversarial Robustness in Machine Learning, Machine Learning and Algorithms, Machine Learning and Data Classification, Advanced Bandit Algorithms Research, Child and Adolescent Psychosocial and Emotional Development, and Adaptive Control of Nonlinear Systems.

Xiaojin Zhu has contributed to numerous publications, some of the recent ones include:

  • Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest, 2020, Environmental Research Letters
  • Differential Patterns of Delayed Emotion Circuit Maturation in Abused Girls With and Without Internalizing Psychopathology, 2021, American Journal of Psychiatry
  • Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning, 2020, arXiv (Cornell University)
  • Adaptive Reward-Poisoning Attacks against Reinforcement Learning, 2020, arXiv (Cornell University)
  • Robust Policy Gradient against Strong Data Corruption, 2021, arXiv (Cornell University)

The frequent publication venues for the scientist include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Environmental Research Letters
  • American Journal of Psychiatry
  • 2022 European Control Conference (ECC)

Collaborations have been established with several researchers, including:

  • Adish Singla
  • Yuzhe Ma
  • Xuezhou Zhang
  • Shubham Bharti
  • Taylor J. Keding

Best Publications

  • Semi-Supervised Learning Literature Survey

    Xiaojin Zhu

  • Semi-supervised learning using Gaussian fields and harmonic functions

    Xiaojin Zhu;Zoubin Ghahramani;John Lafferty

  • Introduction to Semi-Supervised Learning

    Xiaojin Zhu;Andrew B. Goldberg;Ronald Brachman;Thomas Dietterich

  • Learning from labeled and unlabeled data with label propagation

    X Zhu;Z Ghahramani

  • Semi-supervised learning with graphs

    Xiaojin Zhu;John Lafferty;Ronald Rosenfeld

  • Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions

    Xiaojin Zhu;John Lafferty;Zoubin Ghahramani

  • Incorporating domain knowledge into topic modeling via Dirichlet Forest priors

    David Andrzejewski;Xiaojin Zhu;Mark Craven

  • Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization

    Andrew Goldberg;Xiaojin Zhu

  • Learning from Bullying Traces in Social Media

    Jun-Ming Xu;Kwang-Sung Jun;Xiaojin Zhu;Amy Bellmore

  • Using machine teaching to identify optimal training-set attacks on machine learners

    Shike Mei;Xiaojin Zhu

  • A Topic Model for Word Sense Disambiguation

    Jordan Boyd-Graber;David Blei;Xiaojin Zhu

  • Improving Diversity in Ranking using Absorbing Random Walks

    Xiaojin Zhu;Andrew Goldberg;Jurgen Van Gael;David Andrzejewski

  • Corleone: hands-off crowdsourcing for entity matching

    Chaitanya Gokhale;Sanjib Das;AnHai Doan;Jeffrey F. Naughton

  • Machine teaching: an inverse problem to machine learning and an approach toward optimal education

    Xiaojin Zhu

  • Unlabeled data: Now it helps, now it doesn't

    Aarti Singh;Robert Nowak;Xiaojin Zhu

  • Segmenting hands of arbitrary color

    Xiaojin Zhu;Jie Yang;A. Waibel

  • Transduction with Matrix Completion: Three Birds with One Stone

    Andrew Goldberg;Ben Recht;Junming Xu;Robert Nowak

  • Data poisoning attacks against autoregressive models

    Scott Alfeld;Xiaojin Zhu;Paul Barford

  • Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning

    Xiaojin Zhu;John Lafferty

  • Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

    Yanghui Kang;Mutlu Ozdogan;Xiaojin Zhu;Zhiwei Ye

  • Kernel conditional random fields: representation and clique selection

    John Lafferty;Xiaojin Zhu;Yan Liu

Frequent Co-Authors

Timothy T. Rogers
Timothy T. Rogers University of Wisconsin–Madison
John Lafferty
John Lafferty Yale University
Robert Nowak
Robert Nowak University of Wisconsin–Madison
Charles W. Kalish
Charles W. Kalish University of Wisconsin–Madison
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Paul Barford
Paul Barford University of Wisconsin–Madison
Amy Bellmore
Amy Bellmore University of Wisconsin–Madison
Charles R. Dyer
Charles R. Dyer University of Wisconsin–Madison
Roni Rosenfeld
Roni Rosenfeld Carnegie Mellon University
Alex Waibel
Alex Waibel Carnegie Mellon University

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