Wonsang You

ORCID iD
https://orcid.org/0000-0002-6806-7135
  • Country
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United States,

Sources:
Wonsang You (2017-09-01)

South Korea

Sources:
Wonsang You (2017-09-01)

  • Keywords
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neuroimaging,

Sources:
Wonsang You (2017-09-01)

time series analysis,

Sources:
Wonsang You (2017-09-01)

machine learning,

Sources:
Wonsang You (2017-09-01)

placenta,

Sources:
Wonsang You (2017-09-01)

fetal brain,

Sources:
Wonsang You (2017-09-01)

MRI

Sources:
Wonsang You (2017-09-01)

  • Websites
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Homepage

Sources:
Wonsang You (2017-09-01)

Blog

Sources:
Wonsang You (2017-09-01)

LinkedIn

Sources:
Wonsang You (2017-09-01)

Mendeley profile

Sources:
Elsevier - Mendeley (2016-11-18)

  • Other IDs
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ResearcherID: F-2619-2010

Sources:
ResearcherID (2013-03-27)

Loop profile: 21924

Sources:
Loop (2017-09-14)

Scopus Author ID: 56893203300

Sources:
Scopus - Elsevier (2018-01-08)

Biography

Wonsang You received the M.Sc in signal processing from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea, in 2008, working on object detection and motion tracking in image sequences. From 2009 to 2012, he had worked for statistical modeling of resting state functional MRI (rs-fMRI) as a researcher in Leibniz Institute for Neurobiology, Magdeburg, Germany. In 2013, he obtained the Ph.D. degree in electrical engineering and information technology from Otto-von-Guericke University, Magdeburg, Germany. With his dissertation, he suggested a long memory model of rs-fMRI that theoretically describes the physical relationship of fractal behavior with functional connectivity in rs-fMRI. His research interest is to figure out the in utero functional and structural development of fetoplacental circulation and functional brain networks through computational neuroimaging data analyses and to develop its clinical applications for pediatric diagnoses. His specialties include MR imaging data analysis of the placenta and fetal brain, resting state brain connectivity, fractal and wavelet analyses, statistical time series analysis, computational neuroscience, pattern recognition, and machine learning. Since 2013, he has worked as a staff scientist in the Advanced Pediatric Brain Imaging Laboratory at Children’s National Medical Center in Washington D.C.. His current project is to develop analytic tools and data models for in vivo fMRI data of fetal brain and placenta, including fetal motion correction, fetal circulation-based fMRI and diffusion-weighted imaging (DWI) data model of the placenta and fetal brain, and resting state functional connectivity of the fetal brain.
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