World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
9554
World Ranking
9565
National Ranking
4052

Yongyi Yang 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 Yongyi Yang 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: 288 publications — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Yongyi Yang 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 Yongyi Yang 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2021 - IEEE Fellow For contributions to medical image recovery and analysis

Overview

Yongyi Yang is affiliated with the Illinois Institute of Technology in the United States. Their research spans the fields of computer science and medicine, with a strong focus on radiology, nuclear medicine, and imaging. Their work also involves artificial intelligence, molecular biology, computer vision and pattern recognition, and plant science.

Yang's publication record includes contributions to various research topics, including medical imaging techniques and applications, advanced MRI techniques, cardiac imaging and diagnostics, advanced graph neural networks, advanced X-ray and CT imaging, photosynthetic processes and mechanisms, and plant molecular biology research.

They have authored papers in several well-known venues, with frequent publications in arXiv (Cornell University), the Journal of Nuclear Cardiology, Medical Physics, the 2022 IEEE International Conference on Image Processing (ICIP), and Neurocomputing.

Yang's recent papers include:

  • Improving Diagnostic Accuracy in Low-Dose SPECT Myocardial Perfusion Imaging With Convolutional Denoising Networks, 2020, IEEE Transactions on Medical Imaging
  • Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging, 2020, Medical Physics
  • A Low-Cost Multi-Failure Resilient Replication Scheme for High-Data Availability in Cloud Storage, 2020, IEEE/ACM Transactions on Networking
  • Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults, 2021, Human Brain Mapping
  • Regulation of chlorophyll biosynthesis by light-dependent acetylation of NADPH:protochlorophyll oxidoreductase A in Arabidopsis, 2023, Plant Science

The scientist frequently collaborates with several coauthors including P. Hendrik Pretorius, Michael A. King, Miles N. Wernick, Biao Xiong, and Junchi Liu.

Yang was awarded the IEEE Fellow distinction in 2021 for contributions to medical image recovery and analysis.

Best Publications

  • Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

    Jinshan Tang;R.M. Rangayyan;Jun Xu;I. El Naqa

  • A support vector machine approach for detection of microcalcifications

    I. El-Naqa;Yongyi Yang;M.N. Wernick;N.P. Galatsanos

  • Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images

    Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos

  • Vector Space Projections : A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics

    Henry Stark;Yongi Yang;Yongyi Yang

  • Projection-based spatially adaptive reconstruction of block-transform compressed images

    Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos

  • Machine Learning in Medical Imaging

    Miles Wernick;Yongyi Yang;Jovan Brankov;Grigori Yourganov

  • A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications

    Liyang Wei;Yongyi Yang;R.M. Nishikawa;Yulei Jiang

  • Digital watermarking robust to geometric distortions

    Ping Dong;J.G. Brankov;N.P. Galatsanos;Yongyi Yang

  • A similarity learning approach to content-based image retrieval: application to digital mammography

    I. El-Naqa;Yongyi Yang;N.P. Galatsanos;R.M. Nishikawa

  • Multiple-image radiography

    Miles N Wernick;Oliver Wirjadi;Oliver Wirjadi;Dean Chapman;Zhong Zhong

  • Relevance vector machine for automatic detection of clustered microcalcifications

    Liyang Wei;Yongyi Yang;R.M. Nishikawa;M.N. Wernick

  • Removal of compression artifacts using projections onto convex sets and line process modeling

    Yongyi Yang;N.P. Galatsanos

  • Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields

    Yusuf Artan;Masoom A Haider;Deanna L Langer;Theodorus H van der Kwast

  • Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class

    Xin Liu;D.L. Langer;M.A. Haider;Y. Yang

  • Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI.

    Sedat Ozer;Deanna L. Langer;Xin Liu;Masoom A. Haider

  • Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis

    Liyang Wei;Yongyi Yang;Robert M. Nishikawa

  • Tomographic image reconstruction based on a content-adaptive mesh model

    J.G. Brankov;Yongyi Yang;M.N. Wernick

  • A fast approach for accurate content-adaptive mesh generation

    Yongyi Yang;M.N. Wernick;J.G. Brankov

  • Projection-based blind deconvolution

    Yongyi Yang;Nikolas P. Galatsanos;Henry Stark

  • A physical model of multiple-image radiography

    Gocha Khelashvili;Jovan G Brankov;Dean Chapman;Mark A Anastasio

Frequent Co-Authors

Miles N. Wernick
Miles N. Wernick Illinois Institute of Technology
Nikolas P. Galatsanos
Nikolas P. Galatsanos University of Ioannina
Robert M. Nishikawa
Robert M. Nishikawa University of Pittsburgh
Mark A. Anastasio
Mark A. Anastasio University of Illinois at Urbana-Champaign
Piotr J. Slomka
Piotr J. Slomka Cedars-Sinai Medical Center
Aggelos K. Katsaggelos
Aggelos K. Katsaggelos Northwestern University
Stephen C. Strother
Stephen C. Strother University of Toronto
Konstantinos Arfanakis
Konstantinos Arfanakis Illinois Institute of Technology
Aristidis Likas
Aristidis Likas University of Ioannina
David A. Bennett
David A. Bennett Rush University Medical Center

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