World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
21731
World Ranking
11860
National Ranking
4836

Cliff Young 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 Cliff Young 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: 80 publications — 3rd percentile

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

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

Cliff Young 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 Cliff Young 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Cliff Young is affiliated with Google in the United States and is an active researcher primarily in the field of Computer Science. Their published work spans a variety of topics centered on Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Hardware and Architecture, and Cognitive Neuroscience.

Young's research contributions include a number of papers on advanced neural network applications, stochastic gradient optimization techniques, and parallel computing and optimization techniques. Their work also touches on computational physics, advanced memory systems, neural dynamics, and reservoir computing.

Frequent coauthors collaborating with Young include Norman P. Jouppi, George Thomas Kurian, Nishant Patil, Doe Hyun Yoon, and Sheng Li.

They have published articles in several prominent venues, including:

  • IEEE Micro
  • arXiv (Cornell University)
  • Communications of the ACM
  • Nature
  • Computer

Notable recent papers authored or coauthored by Young include:

  • A domain-specific supercomputer for training deep neural networks, 2020, Communications of the ACM
  • The Design Process for Google's Training Chips: TPUv2 and TPUv3, 2021, IEEE Micro
  • Neuromorphic computing at scale, 2025, Nature
  • MegaBlocks: Efficient Sparse Training with Mixture-of-Experts, 2022, arXiv (Cornell University)
  • TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings, 2023, arXiv (Cornell University)

Their main fields of study include:

  • Computer Science

Subfields of study that Cliff Young specializes in cover:

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Cognitive Neuroscience

The central topics addressed in their research are:

  • Advanced Neural Network Applications
  • Stochastic Gradient Optimization Techniques
  • Parallel Computing and Optimization Techniques
  • Computational Physics and Python Applications
  • Advanced Memory and Neural Computing
  • Neural dynamics and brain function
  • Neural Networks and Reservoir Computing

Best Publications

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

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

  • In-Datacenter Performance Analysis of a Tensor Processing Unit

    Norman P. Jouppi;Cliff Young;Nishant Patil;David Patterson

  • In-Datacenter Performance Analysis of a Tensor Processing Unit

    Norman P. Jouppi;Cliff Young;Nishant Patil;David Patterson

  • Anton, a special-purpose machine for molecular dynamics simulation

    David E. Shaw;Martin M. Deneroff;Ron O. Dror;Jeffrey S. Kuskin

  • Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer

    David E. Shaw;J. P. Grossman;Joseph A. Bank;Brannon Batson

  • Millisecond-scale molecular dynamics simulations on Anton

    David E. Shaw;Ron O. Dror;John K. Salmon;J. P. Grossman

  • Embedded Computing: A VLIW Approach to Architecture, Compilers and Tools

    Joseph A. Fisher;Paolo Faraboschi;Cliff Young

  • TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings

    Unknown

  • Ten Lessons From Three Generations Shaped Google’s TPUv4i : Industrial Product

    Norman P. Jouppi;Doe Hyun Yoon;Matthew Ashcraft;Mark Gottscho

  • Motivation for and Evaluation of the First Tensor Processing Unit

    Norman Jouppi;Cliff Young;Nishant Patil;David Patterson

  • A domain-specific supercomputer for training deep neural networks

    Norman P. Jouppi;Doe Hyun Yoon;George Kurian;Sheng Li

  • Mesh-TensorFlow: Deep Learning for Supercomputers

    Noam Shazeer;Youlong Cheng;Niki J. Parmar;Dustin Tran

  • A comparative analysis of schemes for correlated branch prediction

    Cliff Young;Nicolas Gloy;Michael D. Smith

  • A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution

    Jeff Dean;David Patterson;Cliff Young

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

  • A domain-specific architecture for deep neural networks

    Norman P. Jouppi;Cliff Young;Nishant Patil;David Patterson

  • Improving the accuracy of static branch prediction using branch correlation

    Cliff Young;Michael D. Smith

  • MLPerf Training Benchmark

    Peter Mattson;Christine Cheng;Gregory F. Diamos;Cody Coleman

  • Sparse GPU Kernels for Deep Learning

    Trevor Gale;Matei Zaharia;Cliff Young;Erich Elsen

  • The Design Process for Google's Training Chips: TPUv2 and TPUv3

    Thomas Norrie;Nishant Patil;Doe Hyun Yoon;George Kurian

  • Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks

    Soroush Ghodrati;Byung Hoon Ahn;Joon Kyung Kim;Sean Kinzer

  • Instruction scheduling for instruction level parallel processors

    P. Faraboschi;J.A. Fisher;C. Young

Frequent Co-Authors

David E. Shaw
David E. Shaw D. E. Shaw Research
David A. Patterson
David A. Patterson University of California, Berkeley
Norman P. Jouppi
Norman P. Jouppi Google (United States)
Paolo Faraboschi
Paolo Faraboschi Hewlett Packard Enterprise (United States)
Jeffrey Dean
Jeffrey Dean Google (United States)
Matei Zaharia
Matei Zaharia University of California, Berkeley
Nam Sung Kim
Nam Sung Kim University of Illinois at Urbana-Champaign
Hadi Esmaeilzadeh
Hadi Esmaeilzadeh University of California, San Diego
Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Dustin Tran
Dustin Tran Google (United States)

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