World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
7477
World Ranking
8010
National Ranking
3444

Moo K. Chung 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 Moo K. Chung 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: 193 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

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

Moo K. Chung 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 Moo K. Chung 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Moo K. Chung is affiliated with the University of Wisconsin-Madison in the United States. Their research spans areas within computer science and medicine, with a primary focus on computational theory and mathematics as well as radiology, nuclear medicine, and imaging. Their work integrates multiple subfields including cognitive neuroscience, artificial intelligence, and computer vision and pattern recognition.

Their research output covers a range of main topics that include:

  • Topological and Geometric Data Analysis
  • Advanced Neuroimaging Techniques and Applications
  • Functional Brain Connectivity Studies
  • Advanced Graph Neural Networks
  • Bioinformatics and Genomic Networks
  • Brain Tumor Detection and Classification
  • Cell Image Analysis Techniques

Moo K. Chung's recent papers demonstrate ongoing contributions to neuroimaging and computational analysis methods. Recent publications include:

  • "Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis," 2020, IEEE Transactions on Medical Imaging
  • "Topological Data Analysis for Multivariate Time Series Data," 2023, Entropy
  • "Topological Learning and Its Application to Multimodal Brain Network Integration," 2021, Lecture Notes in Computer Science
  • "Fast mesh data augmentation via Chebyshev polynomial of spectral filtering," 2021, Neural Networks
  • "Hodge Laplacian of Brain Networks," 2023, IEEE Transactions on Medical Imaging

The scientist has published extensively in top venues, notably:

  • arXiv (Cornell University)
  • Lecture Notes in Computer Science
  • IEEE Transactions on Medical Imaging
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Human Reproduction

Frequent collaborators include Hernando Ombao, Anqi Qiu, Shih-Gu Huang, Anass B. El-Yaagoubi, and D. Vijay Anand. These coauthors have contributed to multiple joint publications, highlighting collaborative research efforts in brain imaging and computational methods.

Best Publications

  • A unified statistical approach to deformation-based morphometry.

    M.K. Chung;K.J. Worsley;K.J. Worsley;T. Paus;C. Cherif

  • Cortical thickness analysis in autism with heat kernel smoothing.

    Moo K. Chung;Steven M. Robbins;Kim M. Dalton;Richard J. Davidson

  • Deformation-based surface morphometry applied to gray matter deformation.

    Moo K. Chung;Keith J. Worsley;Keith J. Worsley;Steve Robbins;Tomáš Paus

  • SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory

    KJ Worsley;JE Taylor;F Carbonell;MK Chung

  • Integrating VBM into the General Linear Model with Voxelwise Anatomical Covariates

    Terrence R. Oakes;Andrew S. Fox;Tom Johnstone;Moo K. Chung

  • Persistent Brain Network Homology From the Perspective of Dendrogram

    Hyekyoung Lee;Hyejin Kang;M. K. Chung;Bung-Nyun Kim

  • Anatomic development of the oral and pharyngeal portions of the vocal tract: An imaging study

    Houri K. Vorperian;Shubing Wang;Moo K. Chung;E. Michael Schimek

  • Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset.

    Chris Hinrichs;Vikas Singh;Lopamudra Mukherjee;Guofan Xu

  • Weighted Fourier Series Representation and Its Application to Quantifying the Amount of Gray Matter

    M.K. Chung;K.M. Dalton;Li Shen;A.C. Evans

  • Less white matter concentration in autism: 2D voxel-based morphometry.

    Moo K. Chung;Kim M. Dalton;Andrew L. Alexander;Richard J. Davidson

  • Sparse Brain Network Recovery Under Compressed Sensing

    Hyekyoung Lee;Dong Soo Lee;Hyejin Kang;Boong-Nyun Kim

  • General multivariate linear modeling of surface shapes using SurfStat.

    Moo K. Chung;Keith J. Worsley;Brendon M. Nacewicz;Kim M. Dalton

  • Persistence Diagrams of Cortical Surface Data

    Moo K. Chung;Peter Bubenik;Peter T. Kim

  • Tensor-Based Cortical Surface Morphometry via Weighted Spherical Harmonic Representation

    M.K. Chung;K.M. Dalton;R.J. Davidson

  • A study of diffusion tensor imaging by tissue-specific, smoothing-compensated voxel-based analysis.

    Jee Eun Lee;Moo K. Chung;Mariana Lazar;Molly B. DuBray

  • Topology-Based Kernels With Application to Inference Problems in Alzheimer's Disease

    D. Pachauri;C. Hinrichs;M. K. Chung;S. C. Johnson

  • Diffusion smoothing on brain surface via finite element method

    M.K. Chung;J. Taylor

  • Developmental sexual dimorphism of the oral and pharyngeal portions of the vocal tract: An imaging study

    Houri K. Vorperian;Shubing Wang;E. Michael Schimek;Reid B. Durtschi

  • Discriminative persistent homology of brain networks

    Hyekyoung Lee;Moo K. Chung;Hyejin Kang;Bung-Nyun Kim

  • Computing the shape of brain networks using graph filtration and gromov-hausdorff metric

    Hyekyoung Lee;Moo K. Chung;Hyejin Kang;Boong-Nyun Kim

  • The effect of computed tomographic scanner parameters and 3-dimensional volume rendering techniques on the accuracy of linear, angular, and volumetric measurements of the mandible

    Brian J. Whyms;Houri K. Vorperian;Lindell R. Gentry;Eugene M. Schimek

  • Large-Scale Modeling of Parametric Surfaces Using Spherical Harmonics

    Li Shen;M.K. Chung

  • Tracing the evolution of multi-scale functional networks in a mouse model of depression using persistent brain network homology

    Arshi Khalid;Byung Sun Kim;Moo K. Chung;Jong Chul Ye

  • Manifold learning on brain functional networks in aging.

    Anqi Qiu;Anqi Qiu;Annie Lee;Mingzhen Tan;Moo K. Chung

Frequent Co-Authors

Richard J. Davidson
Richard J. Davidson University of Wisconsin–Madison
Andrew L. Alexander
Andrew L. Alexander University of Wisconsin–Madison
Anqi Qiu
Anqi Qiu Hong Kong Polytechnic University
Vikas Singh
Vikas Singh University of Wisconsin–Madison
Seth D. Pollak
Seth D. Pollak University of Wisconsin–Madison
Keith J. Worsley
Keith J. Worsley McGill University
Alan C. Evans
Alan C. Evans McGill University
Dong Soo Lee
Dong Soo Lee Seoul National University
Sterling C. Johnson
Sterling C. Johnson University of Wisconsin–Madison
Li Shen
Li Shen University of Pennsylvania

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