World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
7479
World Ranking
12911
National Ranking
5210

Hao Helen Zhang 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 Hao Helen Zhang 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: 91 publications — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Hao Helen Zhang 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 Hao Helen Zhang 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

Hao Helen Zhang is affiliated with the University of Arizona in the United States. Their research spans multiple interconnected disciplines including Medicine, Biochemistry, Genetics and Molecular Biology, and Computer Science. Within these broader fields, their work often focuses on Molecular Biology, Artificial Intelligence, Infectious Diseases, Computer Vision and Pattern Recognition, and Epidemiology.

The scientist's main research topics involve gene expression and cancer classification, RNA modifications and cancer, COVID-19 clinical research studies, SARS-CoV-2 and COVID-19 research, genomics and phylogenetic studies, bioinformatics and genomic networks, and RNA and protein synthesis mechanisms.

Notable recent publications by Hao Helen Zhang include:

  • Machine Learning Techniques for Optimizing Design of Double T-Shaped Monopole Antenna, 2020, IEEE Transactions on Antennas and Propagation
  • Group IIA secreted phospholipase A2 is associated with the pathobiology leading to COVID-19 mortality, 2021, Journal of Clinical Investigation
  • Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study, 2022, The Lancet Digital Health
  • Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study, 2020, PLoS ONE
  • On fusion methods for knowledge discovery from multi-omics datasets, 2020, Computational and Structural Biotechnology Journal

Frequent co-authors collaborating with Hao Helen Zhang include Brian Hallmark, Guang Yao, Floyd H. Chilton, Laurel Johnstone, and Yinshan Fang.

Publications have appeared in various venues, with numerous contributions to UNC Libraries, arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), IEEE Transactions on Antennas and Propagation, and Nature Communications.

Best Publications

  • On the adaptive elastic-net with a diverging number of parameters

    Hui Zou;Hao Helen Zhang

  • Component selection and smoothing in multivariate nonparametric regression

    Yi Lin;Hao Helen Zhang

  • Adaptive Lasso for Cox's proportional hazards model

    Hao Helen Zhang;Wenbin Lu

  • Principles and Theory for Data Mining and Machine Learning

    Bertrand Clarke;Ernest Fokoue;Hao Helen Zhang

  • Gene selection using support vector machines with non-convex penalty

    Hao Helen Zhang;Jeongyoun Ahn;Xiaodong Lin;Cheolwoo Park

  • Short communication: A new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables

    Huan Liu;Yongqiang Tang;Hao Helen Zhang

  • Machine learning for Big Data analytics in plants

    Chuang Ma;Hao Helen Zhang;Xiangfeng Wang;Xiangfeng Wang

  • Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

    Wei Hsuan Lo-Ciganic;James L. Huang;Hao H. Zhang;Jeremy C. Weiss

  • Variable selection for optimal treatment decision.

    Wenbin Lu;Hao Helen Zhang;Hao Helen Zhang;Donglin Zeng

  • Machine Learning Techniques for Optimizing Design of Double T-Shaped Monopole Antenna

    Yashika Sharma;Hao Helen Zhang;Hao Xin

  • Partially functional linear regression in high dimensions

    Dehan Kong;Kaijie Xue;Fang Yao;Hao H. Zhang

  • Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models

    Hao Helen Zhang;Guang Cheng;Yufeng Liu

  • Interaction Screening for Ultrahigh-Dimensional Data

    Ning Hao;Hao Helen Zhang

  • Hard or Soft Classification? Large-margin Unified Machines.

    Yufeng Liu;Hao Helen Zhang;Yichao Wu

  • Weighted Distance Weighted Discrimination and Its Asymptotic Properties

    Xingye Qiao;Hao Helen Zhang;Yufeng Liu;Michael J. Todd

  • Surface Estimation, Variable Selection, and the Nonparametric Oracle Property

    Curtis B. Storlie;Howard D. Bondell;Brian J. Reich;Hao Helen Zhang

  • Variable selection and model building via likelihood basis pursuit

    Hao Helen Zhang;Grace Wahba;Yi Lin;Meta Voelker

  • Variable selection for the multicategory SVM via adaptive sup-norm regularization

    Hao Helen Zhang;Yufeng Liu;Yichao Wu;Ji Zhu

  • Model Selection for High-Dimensional Quadratic Regression via Regularization

    Ning Hao;Yang Feng;Hao Helen Zhang

  • Support vector machines with adaptive Lq penalty

    Yufeng Liu;Hao Helen Zhang;Cheolwoo Park;Jeongyoun Ahn

  • Sure independence screening for ultrahigh dimensional feature space Discussion

    P Bickel;P Buhlmann;QW Yao;R Samworth

Frequent Co-Authors

Maurizio Del Poeta
Maurizio Del Poeta Stony Brook University
Hansheng Wang
Hansheng Wang Peking University
David S. Alberts
David S. Alberts University of Arizona
Hui Zou
Hui Zou University of Minnesota
Subhashis Ghosal
Subhashis Ghosal North Carolina State University
James Stephen Marron
James Stephen Marron University of North Carolina at Chapel Hill
Charles E. McCall
Charles E. McCall Wake Forest University
Floyd H. Chilton
Floyd H. Chilton University of Arizona
Yusuf A. Hannun
Yusuf A. Hannun Stony Brook University
Hao Xin
Hao Xin University of Arizona

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