World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
32261
World Ranking
5994
National Ranking
2697

Ruoming Pang 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 Ruoming Pang 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 83 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

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

Ruoming Pang 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 Ruoming Pang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Overview

Ruoming Pang is a researcher affiliated with Google in the United States, specializing in computer science with a focus on artificial intelligence and signal processing. Their research encompasses a variety of topics within speech recognition and audio processing, demonstrating significant contributions in these areas.

The main fields of study for Ruoming Pang include:

  • Computer Science

Within this broad field, their subfields of study are:

  • Artificial Intelligence
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Hardware and Architecture

Their work covers several main topics, such as:

  • Speech Recognition and Synthesis
  • Music and Audio Processing
  • Speech and Audio Processing
  • Natural Language Processing Techniques
  • Topic Modeling
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning

Ruoming Pang has a substantial publication record, with frequent appearances in venues like arXiv (Cornell University) and several IEEE conferences. Prominent publication venues include:

  • arXiv (Cornell University)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
  • Interspeech 2022
  • IEEE Journal of Selected Topics in Signal Processing

Notable recent papers authored or co-authored by Ruoming Pang include:

  • "Conformer: Convolution-augmented Transformer for Speech Recognition" (2020, arXiv (Cornell University))
  • "w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training" (2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU))
  • "Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition" (2020, arXiv (Cornell University))
  • "BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition" (2022, IEEE Journal of Selected Topics in Signal Processing)
  • "Vector-quantized Image Modeling with Improved VQGAN" (2021, arXiv (Cornell University))

Collaborations with frequent co-authors include work with the following researchers:

  • Tara N. Sainath
  • Chung-Cheng Chiu
  • Yonghui Wu
  • Wei Han
  • James Qin

Best Publications

  • EfficientDet: Scalable and Efficient Object Detection

    Mingxing Tan;Ruoming Pang;Quoc V. Le

  • Searching for MobileNetV3

    Andrew Howard;Ruoming Pang;Hartwig Adam;Quoc Le

  • MnasNet: Platform-Aware Neural Architecture Search for Mobile

    Mingxing Tan;Bo Chen;Ruoming Pang;Vijay Vasudevan

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

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

  • Searching for MobileNetV3.

    Andrew Howard;Mark Sandler;Grace Chu;Liang-Chieh Chen

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

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

  • Streaming End-to-end Speech Recognition for Mobile Devices

    Yanzhang He;Tara N. Sainath;Rohit Prabhavalkar;Ian McGraw

  • Characteristics of internet background radiation

    Ruoming Pang;Vinod Yegneswaran;Paul Barford;Vern Paxson

  • NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

    Golnaz Ghiasi;Tsung-Yi Lin;Ruoming Pang;Quoc V. Le

  • w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

    Unknown

  • Oblivious hashing: A stealthy software integrity verification primitive

    Yuqun Chen;Ramwarathnam Venkatesan;Matthew Cary;Ruoming Pang

  • The devil and packet trace anonymization

    Ruoming Pang;Mark Allman;Vern Paxson;Jason Lee

  • ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

    Wei Han;Zhengdong Zhang;Yu Zhang;Jiahui Yu

  • A first look at modern enterprise traffic

    Ruoming Pang;Mark Allman;Mike Bennett;Jason Lee

  • Reliability and security in the CoDeeN content distribution network

    Limin Wang;Kyoung Soo Park;Ruoming Pang;Vivek Pai

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

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models

    Jiahui Yu;Pengchong Jin;Hanxiao Liu;Gabriel Bender

  • Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

    Yu Zhang;James Qin;Daniel S. Park;Wei Han

  • A high-level programming environment for packet trace anonymization and transformation

    Ruoming Pang;Vern Paxson

  • A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

    Tara N. Sainath;Yanzhang He;Bo Li;Arun Narayanan

Frequent Co-Authors

Yonghui Wu
Yonghui Wu Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Tara N. Sainath
Tara N. Sainath Google (United States)
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Zhifeng Chen
Zhifeng Chen Google (United States)
Patrick Nguyen
Patrick Nguyen Google (United States)
Liangliang Cao
Liangliang Cao Google (United States)
Vijay K. Vasudevan
Vijay K. Vasudevan Google (United States)
Vern Paxson
Vern Paxson University of California, Berkeley

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