World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
12380
World Ranking
4535
National Ranking
98

Ce 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 Ce 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: 255 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Ce 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 Ce 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: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Ce Zhang is affiliated with ETH Zurich in Switzerland and has a research focus predominantly in the field of computer science, with a substantial body of work in artificial intelligence. Their scholarly output includes 141 publications, with 88 specifically related to artificial intelligence, alongside work in information systems, computer vision and pattern recognition, software, and management science and operations research.

The primary topics of their research cover machine learning and data classification, software engineering research, privacy-preserving technologies in data, adversarial robustness in machine learning, data stream mining techniques, stochastic gradient optimization techniques, and broader machine learning and algorithms.

Ce Zhang has contributed to multiple scholarly venues where their work appears regularly, including:

  • arXiv (Cornell University) with 20 publications
  • Proceedings of the VLDB Endowment with 5 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 4 publications
  • Nature Machine Intelligence with 3 publications
  • Proceedings of the 2022 International Conference on Management of Data with 2 publications

Among their recent papers are:

  • "Advances, challenges and opportunities in creating data for trustworthy AI" (2022) published in Nature Machine Intelligence
  • "RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks" (2020) published in Journal of Chemical Information and Modeling
  • "Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting" (2021) published on arXiv (Cornell University)
  • "Bringing artificial intelligence to business management" (2022) published in Nature Machine Intelligence
  • "DataPerf: Benchmarks for Data-Centric AI Development" (2022) published on arXiv (Cornell University)

Ce Zhang regularly collaborates with a group of frequent co-authors, including Jiawei Jiang, Bojan Karlaš, Bin Cui, Cédric Renggli, and Wentao Wu, with collaboration counts ranging from 7 to 11 publications each.

The scientist has also contributed to academic literature through a book published by Springer Nature titled "Distributed Machine Learning and Gradient Optimization" released in 2022.

Best Publications

  • Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.

    Kun-Hsing Yu;Ce Zhang;Gerald J. Berry;Russ B. Altman

  • Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

    Xiangru Lian;Ce Zhang;Huan Zhang;Cho-Jui Hsieh

  • An object-based convolutional neural network (OCNN) for urban land use classification

    Ce Zhang;Isabel Sargent;Xin Pan;Huapeng Li

  • A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

    Ce Zhang;Xin Pan;Huapeng Li;Andy Gardiner

  • Asynchronous Decentralized Parallel Stochastic Gradient Descent

    Xiangru Lian;Wei Zhang;Ce Zhang;Ji Liu

  • Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

    Defu Cao;Yujing Wang;Juanyong Duan;Ce Zhang

  • Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit

    Kevin Schawinski;Ce Zhang;Hantian Zhang;Lucas Fowler

  • Incremental knowledge base construction using DeepDive

    Jaeho Shin;Sen Wu;Feiran Wang;Christopher De Sa

  • $D^2$: Decentralized Training over Decentralized Data

    Hanlin Tang;Xiangru Lian;Ming Yan;Ce Zhang

  • DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference

    Feng Niu;Ce Zhang;Christopher R;Jude Shavlik

  • Towards Efficient Data Valuation Based on the Shapley Value

    Ruoxi Jia;David Dao;Boxin Wang;Frances Ann Hubis

  • Communication Compression for Decentralized Training

    Hanlin Tang;Shaoduo Gan;Ce Zhang;Tong Zhang

  • Heterogeneity-aware Distributed Parameter Servers

    Jiawei Jiang;Bin Cui;Ce Zhang;Lele Yu

  • ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning

    Hantian Zhang;Jerry Li;Kaan Kara;Dan Alistarh

  • Taming the wild: a unified analysis of HOG WILD! -style algorithms

    Christopher De Sa;Ce Zhang;Kunle Olukotun;Christopher Ré

  • ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading.

    Nora Hollenstein;Jonathan Rotsztejn;Marius Troendle;Andreas Pedroni

  • A Principled Approach to Data Valuation for Federated Learning

    Tianhao Wang;Johannes Rausch;Ce Zhang;Ruoxi Jia

  • DimmWitted: a study of main-memory statistical analytics

    Ce Zhang;Christopher Ré

  • Asynchronous stochastic gradient descent for DNN training

    Shanshan Zhang;Ce Zhang;Zhao You;Rong Zheng

  • Materialization optimizations for feature selection workloads

    Ce Zhang;Arun Kumar;Christopher Ré

  • Brainwash: A data system for feature engineering

    Michael R. Anderson;Dolan Antenucci;Victor Bittorf;Matthew Burgess

  • Efficient task-specific data valuation for nearest neighbor algorithms

    Ruoxi Jia;David Dao;Boxin Wang;Frances Ann Hubis

  • DeepDive: Declarative Knowledge Base Construction

    Christopher De Sa;Alex Ratner;Christopher Ré;Jaeho Shin

  • RAB: Provable Robustness Against Backdoor Attacks

    Maurice Weber;Xiaojun Xu;Bojan Karlas;Ce Zhang

Frequent Co-Authors

Christopher Ré
Christopher Ré Stanford University
Ji Liu
Ji Liu Facebook (United States)
Bin Cui
Bin Cui Peking University
Gustavo Alonso
Gustavo Alonso ETH Zurich
Dan Alistarh
Dan Alistarh Institute of Science and Technology Austria
Jingren Zhou
Jingren Zhou Alibaba Group (China)
Dawn Song
Dawn Song University of California, Berkeley
Yaliang Li
Yaliang Li Alibaba Group (China)
Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison

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