Author

Royoung Kim

Sungkyunkwan UniversityORCID 0000-0002-6223-9258Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:dimensionality reduction· 2 Assay:imaging· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Assay:electronic health records· 1 Approach:experimental assay· 1

Frequent Co-Authors

Won Mok ShimiD· 3 shared Alessia SepeiD· 1 shared Brett D. MenshiD· 1 shared Chi-Ning ChouiD· 1 shared Guo JiahuiiD· 1 shared Haiyan WangiD· 1 shared Jae Young JeoniD· 1 shared James V. HaxbyiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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3 shared papersNeuroscience

Won Mok ShimORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Hyperalignment Reveals Shared Information in Idiosyncratic Fine-Grained Movie fMRI Patterns in Macaques

Yuqi Zhang, Haiyan Wang, Qi Zhu, Xiaolian Li, Jane Han, Guo Jiahui, Won Mok Shim, Royoung Kim, M. Ida Gobbini, Kwon Heo, Alessia Sepe, James V. Haxby, Ma Feilong, Wim Vanduffel

This study uses response hyperalignment to show shared representational spaces in macaque fMRI data despite individual differences in functional topographies.

bioRxiv · Aug 29, 2026 · PreprintNeuroscience

Motion-corrected eye tracking (MoCET) improves gaze accuracy during visual fMRI experiments

Jiwoong Park, Jae Young Jeon, Royoung Kim, Kendrick Kay, Won Mok Shim

The paper introduces MoCET, a method to correct eye tracking drift in fMRI by using head motion parameters, improving gaze accuracy over existing methods.

Nature Communications · Dec 18, 2025Peer-reviewedNeuroscience

Geometry Linked to Untangling Efficiency Reveals Structure and Computation in Neural Populations

Chi-Ning Chou, Royoung Kim, Luke A. Arend, Yao-Yuan Yang, Brett D. Mensh, Won Mok Shim, Matthew G. Perich, SueYeon Chung

This paper introduces GLUE, a framework linking neural activity geometry to computational efficiency, showing task-relevant representations untangle across various domains.

bioRxiv · Feb 26, 2024 · PreprintNeuroscience