World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
69
Citations
13271
World Ranking
2009
National Ranking
1014

Yangkang 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 Yangkang 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: 250 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Yangkang 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 Yangkang 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: 69 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Overview

Yangkang Chen is affiliated with The University of Texas at Austin in the United States. Their research spans several fields, primarily focusing on Earth and Planetary Sciences, Engineering, and Computer Science. Within these broader disciplines, Chen has contributed extensively to subfields such as Geophysics, Artificial Intelligence, Ocean Engineering, Computer Vision and Pattern Recognition, and Mechanical Engineering.

Their scientific output addresses a range of topics, including:

  • Seismic Imaging and Inversion Techniques
  • Seismic Waves and Analysis
  • Seismology and Earthquake Studies
  • Image and Signal Denoising Methods
  • Hydraulic Fracturing and Reservoir Analysis
  • Drilling and Well Engineering
  • Geophysical Methods and Applications

Chen has an extensive publication record appearing frequently in key journals such as:

  • IEEE Transactions on Geoscience and Remote Sensing
  • Geophysics
  • Geophysical Journal International
  • Seismological Research Letters
  • Geophysical Prospecting

Recent representative papers authored or co-authored by Chen include:

  • "Automatic microseismic event picking via unsupervised machine learning," 2020, Geophysical Journal International
  • "Deep denoising autoencoder for seismic random noise attenuation," 2020, Geophysics
  • "Deep learning reservoir porosity prediction based on multilayer long short-term memory network," 2020, Geophysics
  • "Deep-learning seismic full-waveform inversion for realistic structural models," 2020, Geophysics
  • "A fully unsupervised and highly generalized deep learning approach for random noise suppression," 2020, Geophysical Prospecting

Collaborative efforts have involved frequent co-authors, including Omar M. Saad, Sergey Fomel, Liuqing Yang, Hang Wang, and Alexandros Savvaidis. These collaborations encompass multiple papers, indicating ongoing contributions to collective research across geophysical and computational domains.

Best Publications

  • Random noise attenuation using local signal-and-noise orthogonalization

    Yangkang Chen;Sergey B Fomel

  • Deep-Learning Inversion of Seismic Data

    Shucai Li;Bin Liu;Yuxiao Ren;Yangkang Chen

  • Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization

    Yangkang Chen;Sergey B Fomel;Jingwei Hu

  • Random noise attenuation by f-x empirical mode decomposition predictive filtering

    Yangkang Chen;Jitao Ma

  • Damped multichannel singular spectrum analysis for 3D random noise attenuation

    Weilin Huang;Runqiu Wang;Yangkang Chen;Huijian Li

  • Simultaneous denoising and reconstruction of 5-D seismic data via damped rank-reduction method

    Yangkang Chen;Dong Zhang;Zhaoyu Jin;Xiaohong Chen

  • Applications of variational mode decomposition in seismic time-frequency analysis

    Wei Liu;Siyuan Cao;Yangkang Chen

  • Seismic Time–Frequency Analysis via Empirical Wavelet Transform

    Wei Liu;Siyuan Cao;Yangkang Chen

  • Double Sparsity Dictionary for Seismic Noise Attenuation

    Yangkang Chen;Jianwei Ma;Sergey B Fomel

  • Deep denoising autoencoder for seismic random noise attenuation

    Omar M. Saad;Yangkang Chen

  • Deep learning for seismic lithology prediction

    Guoyin Zhang;Zhizhang Wang;Yangkang Chen

  • Dealiased Seismic Data Interpolation Using Seislet Transform With Low-Frequency Constraint

    Shuwei Gan;Shoudong Wang;Yangkang Chen;Yizhuo Zhang

  • Fast dictionary learning for noise attenuation of multidimensional seismic data

    Yangkang Chen

  • Automatic microseismic event picking via unsupervised machine learning

    Yangkang Chen

  • Seismic imaging of incomplete data and simultaneous-source data using least-squares reverse time migration with shaping regularization

    Zhiguang Xue;Yangkang Chen;Sergey B Fomel;Junzhe Sun

  • EMD-seislet transform

    Yangkang Chen;Sergey Fomel

  • Empirical Low-Rank Approximation for Seismic Noise Attenuation

    Yangkang Chen;Yatong Zhou;Wei Chen;Shaohuan Zu

  • An open-source Matlab code package for improved rank-reduction 3D seismic data denoising and reconstruction

    Yangkang Chen;Weilin Huang;Dong Zhang;Wei Chen

  • A method for low-frequency noise suppression based on mathematical morphology in microseismic monitoring

    Huijian Li;Runqiu Wang;Siyuan Cao;Yangkang Chen

  • Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform

    Shuwei Gan;Shoudong Wang;Yangkang Chen;Xiaohong Chen

  • Simultaneous denoising and interpolation of 2D seismic data using data-driven non-negative dictionary learning

    Mohammad Amir Nazari Siahsar;Saman Gholtashi;Vahid Abolghasemi;Yangkang Chen

  • Damped Multichannel Singular Spectrum Analysis for 3D Random Noise Attenuation

    Weilin Huang;Runqui Wang;Ming Zhang;Yangkang Chen

Frequent Co-Authors

Sergey Fomel
Sergey Fomel The University of Texas at Austin
Jianwei Ma
Jianwei Ma Harbin Institute of Technology
Dimitri Komatitsch
Dimitri Komatitsch Aix-Marseille University
Douglas R. Schmitt
Douglas R. Schmitt Purdue University West Lafayette
Jeroen Tromp
Jeroen Tromp Princeton University
Ru-Shan Wu
Ru-Shan Wu University of California, Santa Cruz
Xiang-Yang Li
Xiang-Yang Li China University of Petroleum, Beijing
Norbert Podhorszki
Norbert Podhorszki Oak Ridge National Laboratory
Xinming Wu
Xinming Wu University of Science and Technology of China

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