World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
15765
World Ranking
4004
National Ranking
1909

Yong Fan 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 Yong Fan 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: 205 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Yong Fan 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 Yong Fan 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

Yong Fan is affiliated with the University of Pennsylvania in the United States. Their research primarily spans the fields of Medicine and Neuroscience, with a significant emphasis on Cognitive Neuroscience and Radiology, Nuclear Medicine, and Imaging. Additional subfields in their work include Experimental and Cognitive Psychology, Psychiatry and Mental Health, and Computer Vision and Pattern Recognition.

Their research topics focus mainly on Functional Brain Connectivity Studies, Advanced Neuroimaging Techniques and Applications, Mental Health Research Topics, Neural Dynamics and Brain Function, Advanced MRI Techniques and Applications, Radiomics and Machine Learning in Medical Imaging, and Dementia and Cognitive Impairment Research.

Yong Fan has contributed to various scholarly works published in reputable venues. The most frequent publication venues include bioRxiv (Cold Spring Harbor Laboratory), Biological Psychiatry, arXiv (Cornell University), International Journal of Radiation Oncology*Biology*Physics, and JAMA Psychiatry.

Some of their recent papers are as follows:

  • "MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14,468 individuals worldwide" (2020) Brain
  • "Individual Variation in Functional Topography of Association Networks in Youth" (2020) Neuron
  • "Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning" (2020) Brain
  • "Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum" (2022) JAMA Neurology
  • "The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans" (2020) Alzheimer's & Dementia

Their frequent co-authors include Christos Davatzikos, Theodore D. Satterthwaite, Russell T. Shinohara, Hongming Li, and Güray Erus.

Best Publications

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • A deep learning model integrating FCNNs and CRFs for brain tumor segmentation.

    Xiaomei Zhao;Yihong Wu;Guidong Song;Zhenye Li

  • Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI

    Chandan Misra;Yong Fan;Christos Davatzikos

  • Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection

    Christos Davatzikos;Kosha Ruparel;Yong Fan;Dinggang Shen

  • Gender difference in neural response to psychological stress

    Jiongjiong Wang;Marc Korczykowski;Hengyi Rao;Yong Fan

  • Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline.

    Yong Fan;Nematollah Batmanghelich;Chris M. Clark;Christos Davatzikos

  • Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

    Christos Davatzikos;Yong Fan;Xiaoying Wu;Dinggang Shen

  • Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.

    Raymond Pomponio;Guray Erus;Mohamad Habes;Jimit Doshi

  • A modified Gabor filter design method for fingerprint image enhancement

    Jianwei Yang;Lifeng Liu;Tianzi Jiang;Yong Fan

  • COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements

    Yong Fan;Dinggang Shen;R.C. Gur;R.E. Gur

  • Group information guided ICA for fMRI data analysis

    Yuhui Du;Yong Fan

  • MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

    Vishnu M Bashyam;Guray Erus;Jimit Doshi;Mohamad Habes

  • Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study

    Yong Fan;Susan M. Resnick;Xiaoying Wu;Christos Davatzikos

  • Individual Variation in Functional Topography of Association Networks in Youth

    Zaixu Cui;Hongming Li;Cedric H. Xia;Bart Larsen

  • Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

    Ganesh B Chand;Dominic B Dwyer;Guray Erus;Aristeidis Sotiras;Aristeidis Sotiras

  • Brain anatomical networks in early human brain development.

    Yong Fan;Feng Shi;Jeffrey Keith Smith;Weili Lin

  • A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data

    Hongming Li;Mohamad Habes;David A. Wolk;Yong Fan

  • Neonatal Brain Image Segmentation in Longitudinal MRI Studies

    Feng Shi;Yong Fan;Songyuan Tang;John H. Gilmore

  • High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables

    Ying Wang;Yong Fan;Priyanka Bhatt;Christos Davatzikos

  • Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

    Christos Davatzikos;Saima Rathore;Spyridon Bakas;Sarthak Pati

  • Development trends of white matter connectivity in the first years of life.

    Pew Thian Yap;Yong Fan;Yasheng Chen;John H. Gilmore

Frequent Co-Authors

Christos Davatzikos
Christos Davatzikos University of Pennsylvania
Dinggang Shen
Dinggang Shen ShanghaiTech University
Tianzi Jiang
Tianzi Jiang Chinese Academy of Sciences
Theodore D. Satterthwaite
Theodore D. Satterthwaite University of Pennsylvania
Ruben C. Gur
Ruben C. Gur University of Pennsylvania
Raquel E. Gur
Raquel E. Gur University of Pennsylvania
Daniel H. Wolf
Daniel H. Wolf University of Pennsylvania
Russell T. Shinohara
Russell T. Shinohara University of Pennsylvania
Nikolaos Koutsouleris
Nikolaos Koutsouleris Ludwig-Maximilians-Universität München
Susan M. Resnick
Susan M. Resnick National Institutes of Health

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