World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
10362
World Ranking
8231
National Ranking
3530

Jinfeng Yi 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 Jinfeng Yi 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: 107 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

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

Jinfeng Yi 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 Jinfeng Yi 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Jinfeng Yi is a researcher affiliated with IBM in the United States, with a primary focus on computer science and its applied domains. Their work reflects a strong emphasis on artificial intelligence, with significant contributions spanning several subfields including computer vision and pattern recognition, cancer research, molecular biology, and signal processing.

Their recent scholarly output includes publications on topics such as adversarial robustness, privacy-preserving technologies, and advanced neural network applications. Notable papers authored or co-authored by Yi include:

  • Trustworthy AI: From Principles to Practices (2022) published in ACM Computing Surveys
  • On the Convergence and Robustness of Adversarial Training (2021) published in arXiv (Cornell University)
  • Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples (2020) published in the Proceedings of the AAAI Conference on Artificial Intelligence
  • Creatine kinase B suppresses ferroptosis by phosphorylating GPX4 through a moonlighting function (2023) published in Nature Cell Biology
  • Federated User Modeling from Hierarchical Information (2023) published in ACM Transactions on Information Systems

Yi's extensive publication record includes frequent contributions to venues such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, Breast Cancer Research and Treatment, ACM Computing Surveys, and Nature Cell Biology.

The main research fields represented in Yi's work are:

  • Computer Science

With deeper specialization in the following subfields:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Cancer Research
  • Molecular Biology
  • Signal Processing

Yi's research covers a variety of key topics, including:

  • Adversarial Robustness in Machine Learning
  • Domain Adaptation and Few-Shot Learning
  • Anomaly Detection Techniques and Applications
  • Privacy-Preserving Technologies in Data
  • Advanced Neural Network Applications
  • Advanced Malware Detection Techniques
  • Stochastic Gradient Optimization Techniques

Frequent collaborators of Jinfeng Yi are Cho-Jui Hsieh, Pin-Yu Chen, Lue Tao, Songcan Chen, and Lijun Zhang, reflecting strong co-authorship ties in their research community.

Best Publications

  • ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models

    Pin-Yu Chen;Huan Zhang;Yash Sharma;Jinfeng Yi

  • Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques

    J. Yi;T. Nasukawa;R. Bunescu;W. Niblack

  • Symmetric Cross Entropy for Robust Learning With Noisy Labels

    Yisen Wang;Xingjun Ma;Zaiyi Chen;Yuan Luo

  • EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

    Pin-Yu Chen;Yash Sharma;Huan Zhang;Jinfeng Yi

  • Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

    Tianxin Wei;Fuli Feng;Jiawei Chen;Ziwei Wu

  • Trustworthy AI: From Principles to Practices.

    Bo Li;Peng Qi;Bo Liu;Shuai Di

  • AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks

    Chun-Chen Tu;Paishun Ting;Pin-Yu Chen;Sijia Liu

  • Is Robustness the Cost of Accuracy? – A Comprehensive Study on the Robustness of 18 Deep Image Classification Models

    Dong Su;Huan Zhang;Hongge Chen;Jinfeng Yi

  • Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

    Tsui-Wei Weng;Huan Zhang;Pin-Yu Chen;Jinfeng Yi

  • Improving Adversarial Robustness Requires Revisiting Misclassified Examples

    Yisen Wang;Difan Zou;Jinfeng Yi;James Bailey

  • Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach

    Minhao Cheng;Thong Le;Pin-Yu Chen;Jinfeng Yi

  • Diverse Few-Shot Text Classification with Multiple Metrics

    Mo Yu;Xiaoxiao Guo;Jinfeng Yi;Shiyu Chang

  • Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples

    Minhao Cheng;Jinfeng Yi;Pin-Yu Chen;Huan Zhang

  • On the convergence and robustness of adversarial training

    Yisen Wang;Xingjun Ma;James Bailey;Jinfeng Yi

  • Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning

    Hongge Chen;Huan Zhang;Pin-Yu Chen;Jinfeng Yi

  • Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (SGD)

    Qi Qian;Rong Jin;Jinfeng Yi;Lijun Zhang

  • Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning

    Jinfeng Yi;Rong Jin;Shaili Jain;Tianbao Yang

  • Robust Ensemble Clustering by Matrix Completion

    Jinfeng Yi;Tianbao Yang;Rong Jin;Anil K. Jain

  • Tracking slowly moving clairvoyant: optimal dynamic regret of online learning with true and noisy gradient

    Tianbao Yang;Lijun Zhang;Rong Jin;Jinfeng Yi

  • Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

    Zhao Kang;Xiao Lu;Jinfeng Yi;Zenglin Xu

  • Improved Dynamic Regret for Non-degenerate Functions

    Lijun Zhang;Tianbao Yang;Jinfeng Yi;Rong Jin

Frequent Co-Authors

Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Huan Zhang
Huan Zhang University of California, Los Angeles
Rong Jin
Rong Jin Alibaba Group (China)
Pin-Yu Chen
Pin-Yu Chen IBM (United States)
Tianbao Yang
Tianbao Yang Texas A&M University
Bowen Zhou
Bowen Zhou IBM (United States)
Zhi-Hua Zhou
Zhi-Hua Zhou Nanjing University
Jian Zhang
Jian Zhang University of Technology Sydney
Anil K. Jain
Anil K. Jain Michigan State University
Kush R. Varshney
Kush R. Varshney IBM (United States)

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