World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
8915
World Ranking
13831
National Ranking
1677

Tianshi Chen 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 Tianshi Chen 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: 95 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Tianshi Chen 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 Tianshi Chen 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: 30 D-Index — 3rd percentile

3% of scientists in this discipline score the same or lower.

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

Overview

Tianshi Chen is affiliated with the Chinese Academy of Sciences in China. Their research spans primarily within the fields of Computer Science and Engineering, with a notable focus on subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Hardware and Architecture, and Control and Systems Engineering.

The core topics of their work encompass Advanced Neural Network Applications, Control Systems and Identification, Neural Networks and Applications, Advanced Memory and Neural Computing, Human Pose and Action Recognition, Model Reduction and Neural Networks, and Gaussian Processes and Bayesian Inference.

Chen's publication record includes contributions to several frequent venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Computers
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Automatica
  • IEEE Transactions on Image Processing

Their recent papers include:

  • "Distilling Object Detectors with Feature Richness," 2021, arXiv (Cornell University)
  • "DWM: A Decomposable Winograd Method for Convolution Acceleration," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "An efficient implementation for spatial-temporal Gaussian process regression and its applications," 2022, Automatica
  • "Machine Learning Computers With Fractal von Neumann Architecture," 2020, IEEE Transactions on Computers
  • "Addressing Irregularity in Sparse Neural Networks through a Cooperative Software/Hardware Approach," 2020, IEEE Transactions on Computers

Frequent co-authors collaborating with Chen include:

  • Zidong Du
  • Xishan Zhang
  • Shaoli Liu
  • Yunji Chen
  • Tian Zhi

Best Publications

  • DaDianNao: A Machine-Learning Supercomputer

    Yunji Chen;Tao Luo;Shaoli Liu;Shijin Zhang

  • DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning

    Tianshi Chen;Zidong Du;Ninghui Sun;Jia Wang

  • ShiDianNao: shifting vision processing closer to the sensor

    Zidong Du;Robert Fasthuber;Tianshi Chen;Paolo Ienne

  • Cambricon-X: An accelerator for sparse neural networks

    Unknown

  • Cambricon-x: an accelerator for sparse neural networks

    Shijin Zhang;Zidong Du;Lei Zhang;Huiying Lan

  • PuDianNao: A Polyvalent Machine Learning Accelerator

    Daofu Liu;Tianshi Chen;Shaoli Liu;Jinhong Zhou

  • Cambricon: an instruction set architecture for neural networks

    Shaoli Liu;Zidong Du;Jinhua Tao;Dong Han

  • DianNao

    Unknown

  • DianNao

    Unknown

  • DianNao family: energy-efficient hardware accelerators for machine learning

    Yunji Chen;Tianshi Chen;Zhiwei Xu;Ninghui Sun

  • DaDianNao: A Neural Network Supercomputer

    Tao Luo;Shaoli Liu;Ling Li;Yuqing Wang

  • Cambricon-s: addressing irregularity in sparse neural networks through a cooperative software/hardware approach

    Xuda Zhou;Zidong Du;Qi Guo;Shaoli Liu

  • A large population size can be unhelpful in evolutionary algorithms

    Tianshi Chen;Ke Tang;Guoliang Chen;Xin Yao

  • BenchNN: On the broad potential application scope of hardware neural network accelerators

    Tianshi Chen;Yunji Chen;Marc Duranton;Qi Guo

  • ShiDianNao

    Unknown

  • Cambricon: An Instruction Set Architecture for Neural Networks

    Unknown

  • Analysis of Computational Time of Simple Estimation of Distribution Algorithms

    Tianshi Chen;Ke Tang;Guoliang Chen;Xin Yao

  • Scaling Up Estimation of Distribution Algorithms for Continuous Optimization

    Weishan Dong;Tianshi Chen;Peter Tino;Xin Yao

  • A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems

    Tianshi Chen;Jun He;Guangzhong Sun;Guoliang Chen

  • A multi-objective approach to Redundancy Allocation Problem in parallel-series systems

    Zai Wang;Tianshi Chen;Ke Tang;Xin Yao

  • Neuromorphic accelerators: a comparison between neuroscience and machine-learning approaches

    Zidong Du;Daniel D Ben-Dayan Rubin;Yunji Chen;Liqiang Hel

  • PuDianNao

    Unknown

  • Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization

    Xin Yu;Ke Tang;Tianshi Chen;Xin Yao;Xin Yao

  • Effective and efficient microprocessor design space exploration using unlabeled design configurations

    Tianshi Chen;Yunji Chen;Qi Guo;Zhi-Hua Zhou

  • When is an estimation of distribution algorithm better than an evolutionary algorithm

    Tianshi Chen;Per Kristian Lehre;Ke Tang;Xin Yao

  • Deterministic Replay: A Survey

    Yunji Chen;Shijin Zhang;Qi Guo;Ling Li

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Olivier Temam
Olivier Temam DeepMind (United Kingdom)
Ke Tang
Ke Tang Southern University of Science and Technology
Zhi-Hua Zhou
Zhi-Hua Zhou Nanjing University
Peter Tino
Peter Tino University of Birmingham
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Haibo Chen
Haibo Chen Shanghai Jiao Tong University
Yuan Xie
Yuan Xie Hong Kong University of Science and Technology
Paolo Ienne
Paolo Ienne École Polytechnique Fédérale de Lausanne
Rui Zhang
Rui Zhang National University of Singapore

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