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Computer Science

D-Index
43
Citations
7477
World Ranking
8011
National Ranking
3444

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
Alan C. Evans
Alan C. Evans McGill University
Li Shen
Li Shen University of Pennsylvania
Carien M. van Reekum
Carien M. van Reekum University of Reading
Martin A. Lindquist
Martin A. Lindquist Johns Hopkins University
H. Hill Goldsmith
H. Hill Goldsmith University of Wisconsin–Madison

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