World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
18320
World Ranking
1698
National Ranking
866

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
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
Dong Xu
Dong Xu University of Missouri
Richard M. Karp
Richard M. Karp University of California, Berkeley
Rui Jiang
Rui Jiang Beijing Jiaotong University

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