World's Best Scientists 2026 revealed!
Warren J. Gross

Warren J. Gross

D-Index & Metrics

Computer Science

D-Index
52
Citations
10540
World Ranking
5099
National Ranking
200

Warren J. Gross 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 Warren J. Gross 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: 326 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Warren J. Gross 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 Warren J. Gross 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: 52 D-Index — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Overview

Warren J. Gross is affiliated with McGill University in Canada and has a significant body of research primarily in the fields of Computer Science and Engineering. Their work spans a broad range of subfields including Artificial Intelligence, Electrical and Electronic Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition, and Molecular Biology.

The scientist's research focuses extensively on topics such as Error Correcting Code Techniques, Advanced Wireless Communication Techniques, Coding Theory and Cryptography, Advanced Neural Network Applications, Cooperative Communication and Network Coding, DNA and Biological Computing, and Domain Adaptation and Few-Shot Learning.

Recent notable papers include:

  • High-Throughput and Energy-Efficient VLSI Architecture for Ordered Reliability Bits GRAND, 2022, IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • Practical Dynamic SC-Flip Polar Decoders: Algorithm and Implementation, 2020, IEEE Transactions on Signal Processing
  • Introduction to Dynamic Stochastic Computing, 2020, IEEE Circuits and Systems Magazine
  • Efficient Fine-Tuning of BERT Models on the Edge, 2022, 2022 IEEE International Symposium on Circuits and Systems (ISCAS)
  • A Design Framework for Invertible Logic, 2020, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Frequent co-authors collaborating with this scientist include:

  • Brett H. Meyer
  • James J. Clark
  • Marwan Jalaleddine
  • Huayi Zhou
  • Syed Mohsin Abbas

Publications are often found in these venues:

  • arXiv (Cornell University)
  • Journal of Signal Processing Systems
  • IEEE Communications Letters
  • IEEE Access
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Best Publications

  • Deep Learning Methods for Improved Decoding of Linear Codes

    Eliya Nachmani;Elad Marciano;Loren Lugosch;Warren J. Gross

  • Fast Polar Decoders: Algorithm and Implementation

    Gabi Sarkis;Pascal Giard;Alexander Vardy;Claude Thibeault

  • A Semi-Parallel Successive-Cancellation Decoder for Polar Codes

    C. Leroux;A. J. Raymond;G. Sarkis;W. J. Gross

  • Hardware architectures for successive cancellation decoding of polar codes

    Camille Leroux;Ido Tal;Alexander Vardy;Warren J. Gross

  • VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing

    Arash Ardakani;Francois Leduc-Primeau;Naoya Onizawa;Takahiro Hanyu

  • Fully Parallel Stochastic LDPC Decoders

    S. Sharifi Tehrani;S. Mannor;W.J. Gross

  • Fast List Decoders for Polar Codes

    Gabi Sarkis;Pascal Giard;Alexander Vardy;Claude Thibeault

  • Fast and Flexible Successive-Cancellation List Decoders for Polar Codes

    Seyyed Ali Hashemi;Carlo Condo;Warren J. Gross

  • Stochastic decoding of LDPC codes

    S. Sharifi Tehrani;W.J. Gross;S. Mannor

  • Simplified MAP algorithm suitable for implementation of turbo decoders

    W.J. Gross;P.G. Gulak

  • Delayed Stochastic Decoding of LDPC Codes

    A. Naderi;S. Mannor;M. Sawan;W. J. Gross

  • Hardware Architecture for List Successive Cancellation Decoding of Polar Codes

    Alexios Balatsoukas-Stimming;Alexandre J. Raymond;Warren J. Gross;Andreas Burg

  • Majority-Based Tracking Forecast Memories for Stochastic LDPC Decoding

    Saeed Sharifi Tehrani;Ali Naderi;Guy-Armand Kamendje;Saied Hemati

  • VLSI architectures for the MAP algorithm

    E. Boutillon;W.J. Gross;P.G. Gulak

  • Neural offset min-sum decoding

    Loren Lugosch;Warren J. Gross

  • Increasing the Throughput of Polar Decoders

    Gabi Sarkis;Warren J. Gross

  • Flexible and Low-Complexity Encoding and Decoding of Systematic Polar Codes

    Gabi Sarkis;Ido Tal;Pascal Giard;Alexander Vardy

  • Methods and systems for decoding polar codes

    Warren Gross;Gabi Sarkis;Alexandre Raymond;Camille Leroux

  • A successive cancellation decoder ASIC for a 1024-bit polar code in 180nm CMOS

    A. Mishra;A. J. Raymond;L. G. Amaru;G. Sarkis

  • Hardware Implementation of Successive-Cancellation Decoders for Polar Codes

    Camille Leroux;Alexandre J. Raymond;Gabi Sarkis;Ido Tal

  • An Architecture to Accelerate Convolution in Deep Neural Networks

    Arash Ardakani;Carlo Condo;Mehdi Ahmadi;Warren J. Gross

Frequent Co-Authors

Takahiro Hanyu
Takahiro Hanyu Tohoku University
Shie Mannor
Shie Mannor Technion – Israel Institute of Technology
Alexander Vardy
Alexander Vardy University of California, San Diego
Frank R. Kschischang
Frank R. Kschischang University of Toronto
P.G. Gulak
P.G. Gulak University of Toronto
Andreas Burg
Andreas Burg École Polytechnique Fédérale de Lausanne
Xiaohu You
Xiaohu You Southeast University
Zaichen Zhang
Zaichen Zhang Southeast University
Ralf Koetter
Ralf Koetter Technical University of Munich
Michael Rabbat
Michael Rabbat Facebook (United States)

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