World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
70324
World Ranking
4415
National Ranking
2061

Zhifeng 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 Zhifeng 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: 192 publications — 43rd percentile

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

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

Zhifeng 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 Zhifeng 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: 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

Zhifeng Chen is affiliated with Google in the United States and conducts research primarily in the field of Computer Science. Their work spans several subfields including Computer Networks and Communications, Plant Science, Artificial Intelligence, Signal Processing, and Molecular Biology.

The scientist's research topics cover a broad range of areas such as Plant Physiology and Cultivation Studies, Phytochemicals and Antioxidant Activities, Speech Recognition and Synthesis, Music and Audio Processing, Speech and Audio Processing, Autonomous Vehicle Technology and Safety, and Nerve Injury and Regeneration.

Zhifeng Chen has published numerous papers in various venues, with a notable focus on contributions to arXiv (Cornell University). Frequent publication venues include:

  • arXiv (Cornell University)
  • iScience
  • LWT
  • IEEE Journal of Selected Topics in Signal Processing
  • Applied Sciences

Recent papers authored or co-authored by Zhifeng Chen demonstrate engagement with contemporary challenges in machine learning, speech processing, and autonomous systems. Selected recent publications include:

  • BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2022), IEEE Journal of Selected Topics in Signal Processing
  • Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning (2022), arXiv (Cornell University)
  • Scene Transformer: A unified architecture for predicting multiple agent trajectories (2021), arXiv (Cornell University)
  • GSPMD: General and Scalable Parallelization for ML Computation Graphs (2021), arXiv (Cornell University)
  • AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving (2023), arXiv (Cornell University)

Zhifeng Chen frequently collaborates with several researchers, including V.H. Escalona, Huanxiu Li, Bo Sun, Kehao Liang, and Hongmei Di.

Best Publications

  • TensorFlow: a system for large-scale machine learning

    Martín Abadi;Paul Barham;Jianmin Chen;Zhifeng Chen

  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le

  • Scalability in Perception for Autonomous Driving: Waymo Open Dataset

    Pei Sun;Henrik Kretzschmar;Xerxes Dotiwalla;Aurelien Chouard

  • Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

    Jonathan Shen;Ruoming Pang;Ron J. Weiss;Mike Schuster

  • Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

    Melvin Johnson;Mike Schuster;Quoc V. Le;Maxim Krikun

  • Tacotron: Towards End-to-End Speech Synthesis

    Yuxuan Wang;R. J. Skerry-Ryan;Daisy Stanton;Yonghui Wu

  • State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

    Chung-Cheng Chiu;Tara N. Sainath;Yonghui Wu;Rohit Prabhavalkar

  • GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

    Yanping Huang;Youlong Cheng;Ankur Bapna;Orhan Firat

  • Fast and memory-efficient regular expression matching for deep packet inspection

    Fang Yu;Zhifeng Chen;Yanlei Diao;T. V. Lakshman

  • LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

    Heiga Zen;Viet Dang;Rob Clark;Yu Zhang

  • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

    Ye Jia;Yu Zhang;Ron J. Weiss;Quan Wang

  • PaLM 2 Technical Report

    Unknown

  • Hibernator: helping disk arrays sleep through the winter

    Qingbo Zhu;Zhifeng Chen;Lin Tan;Yuanyuan Zhou

  • GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

    Dmitry Lepikhin;HyoukJoong Lee;Yuanzhong Xu;Dehao Chen

  • The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation

    Mia Xu Chen;Orhan Firat;Ankur Bapna;Melvin Johnson

  • Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

    Naveen Arivazhagan;Ankur Bapna;Orhan Firat;Dmitry Lepikhin

  • Sequence-to-Sequence Models Can Directly Translate Foreign Speech

    Ron J. Weiss;Jan Chorowski;Navdeep Jaitly;Yonghui Wu

  • GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

    Yanping Huang;Youlong Cheng;Ankur Bapna;Orhan Firat

  • Scalability in Perception for Autonomous Driving: Waymo Open Dataset

    Pei Sun;Henrik Kretzschmar;Xerxes Dotiwalla;Aurelien Chouard

  • C-Miner: Mining Block Correlations in Storage Systems

    Zhenmin Li;Zhifeng Chen;Sudarshan M. Srinivasan;Yuanyuan Zhou

  • Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

    Anjuli Kannan;Yonghui Wu;Patrick Nguyen;Tara N. Sainath

Frequent Co-Authors

Yonghui Wu
Yonghui Wu Google (United States)
Patrick Nguyen
Patrick Nguyen Google (United States)
Tara N. Sainath
Tara N. Sainath Google (United States)
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Navdeep Jaitly
Navdeep Jaitly Google (United States)
Ruoming Pang
Ruoming Pang Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Vijay K. Vasudevan
Vijay K. Vasudevan Google (United States)
Yuxuan Wang
Yuxuan Wang ByteDance

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