World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
18320
World Ranking
1698
National Ranking
867

Tao Jiang 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 Tao Jiang 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: 443 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Tao Jiang 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 Tao Jiang 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: 72 D-Index — 89th percentile

89% 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

  • 2019 - IEEE Fellow For contributions to coding, modulation, and cognitive radio systems design
  • 2007 - ACM Fellow For contributions to computational biology and computational complexity.
  • 2006 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Tao Jiang is affiliated with the University of California, Riverside, in the United States. Their research spans multiple academic disciplines, with significant contributions in computer science, environmental science, and biochemistry, genetics, and molecular biology. Subfields of their expertise include molecular biology, health, toxicology and mutagenesis, artificial intelligence, computational theory and mathematics, and atmospheric science.

Their body of work addresses various scientific topics. Notable areas of focus include mercury impact and mitigation studies, limits and structures in graph theory, advanced graph theory research, heavy metals in the environment, RNA modifications and cancer, RNA and protein synthesis mechanisms, and heavy metal exposure and toxicity.

Tao Jiang has published extensively across several venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Remote Sensing
  • Journal of Hazardous Materials
  • Environmental Science & Technology

Selected recent papers illustrate the range of their research interests:

  • MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities, 2020, Cell Systems
  • Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques, 2021, International Journal of Medical Informatics
  • Somatic SF3B1 hotspot mutation in prolactinomas, 2020, Nature Communications
  • Algal Organic Matter Drives Methanogen-Mediated Methylmercury Production in Water from Eutrophic Shallow Lakes, 2021, Environmental Science & Technology
  • A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories, 2020, Nature Machine Intelligence

Collaboration is an element of Tao Jiang's research activities. Frequent coauthors include:

  • Xingming Zheng
  • Xiaofeng Li
  • Dingyong Wang
  • Jianyang Zeng
  • Lingjia Gu

Awards recognizing contributions to science have been granted to Tao Jiang. These include the IEEE Fellow award in 2019 for work in coding, modulation, and cognitive radio systems design; the ACM Fellow award in 2007 for contributions to computational biology and computational complexity; and election as a Fellow of the American Association for the Advancement of Science (AAAS) in 2006.

Best Publications

  • On the Complexity of Multiple Sequence Alignment

    Lusheng Wang;Tao Jiang

  • Efficient and robust feature extraction by maximum margin criterion

    Haifeng Li;Tao Jiang;Keshu Zhang

  • Alignment of trees: an alternative to tree edit

    Tao Jiang;Lusheng Wang;Kaizhong Zhang

  • IsoLasso: a LASSO regression approach to RNA-Seq based transcriptome assembly.

    Wei Li;Jianxing Feng;Tao Jiang

  • Linear approximation of shortest superstrings

    Avrim Blum;Tao Jiang;Ming Li;John Tromp

  • Minimal NFA problems are hard

    Tao Jiang;B. Ravikumar

  • ChemmineR: a compound mining framework for R

    Yiqun Cao;Anna Charisi;Li-Chang Cheng;Tao Jiang

  • NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions.

    Fangping Wan;Lixiang Hong;An Xiao;Tao Jiang

  • On the complexity of comparing evolutionary trees

    Jotun Hein;Tao Jiang;Lusheng Wang;Kaizhong Zhang

  • On the Approximation of Shortest Common Supersequencesand Longest Common Subsequences

    Tao Jiang;Ming Li

  • A general edit distance between RNA structures.

    Tao Jiang;Guohui Lin;Bin Ma;Kaizhong Zhang

  • A maximum common substructure-based algorithm for searching and predicting drug-like compounds

    Yiqun Cao;Tao Jiang;Thomas Girke

  • SCALE method for single-cell ATAC-seq analysis via latent feature extraction.

    Lei Xiong;Kui Xu;Kang Tian;Yanqiu Shao

  • MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities

    Shuya Li;Fangping Wan;Hantao Shu;Tao Jiang;Tao Jiang

  • Assignment of Orthologous Genes via Genome Rearrangement

    Xin Chen;Jie Zheng;Zheng Fu;Peng Nan

  • Current Topics in Computational Molecular Biology

    Tao Jiang;Michael Zhang;Ying Xu

  • Some MAX SNP-hard results concerning unordered labeled trees

    Kaizhong Zhang;Tao Jiang

  • Lower Bounds on Learning Decision Lists and Trees

    Thomas Hancock;Tao Jiang;Ming Li;John Tromp

  • Analysis of Bacterial Community Composition by Oligonucleotide Fingerprinting of rRNA Genes

    Lea Valinsky;Gianluca Della Vedova;Alexandra J. Scupham;Sam Alvey

  • The longest common subsequence problem for sequences with nested arc annotations

    Guohui Lin;Zhi-Zhong Chen;Tao Jiang;Jianjun Wen

Frequent Co-Authors

Ying Xu
Ying Xu University of Georgia
Guohui Lin
Guohui Lin University of Alberta
Oscar H. Ibarra
Oscar H. Ibarra University of California, Santa Barbara
Michael Q. Zhang
Michael Q. Zhang The University of Texas at Dallas
Paul M. B. Vitányi
Paul M. B. Vitányi Centrum Wiskunde & Informatica
James Borneman
James Borneman University of California, Riverside
Lusheng Wang
Lusheng Wang City University of Hong Kong
Stefano Lonardi
Stefano Lonardi University of California, Riverside
Richard M. Karp
Richard M. Karp University of California, Berkeley
Dong Xu
Dong Xu University of Missouri

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