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YongKeun Park

Researcher at KAIST

Publications -  420
Citations -  14089

YongKeun Park is an academic researcher from KAIST. The author has contributed to research in topics: Scattering & Light scattering. The author has an hindex of 58, co-authored 378 publications receiving 11459 citations. Previous affiliations of YongKeun Park include Sungkyunkwan University & Massachusetts Institute of Technology.

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Method and apparatus for generating 3-d molecular image based on label-free method using 3-d refractive index image and deep learning

TL;DR: In this article, a method and apparatus for generating a 3D molecular image based on a label-free method using a 3-D refractive index image and deep learning is presented.
Journal ArticleDOI

Pulse-to-pulse field characterization at X-ray free-electron lasers using a speckle-correlation scattering matrix

KyeoReh Lee, +2 more
- 22 Feb 2023 - 
TL;DR: In this article , a speckle-correlation scattering matrix is proposed to reconstruct the complex field without sample constraints or multiple acquisitions by introducing a designed diffuser before the detector, which can readily serve as an on-field and real-time pulse diagnostic tool at XFELs.
Proceedings ArticleDOI

Optogenetic regulation of cellular functions through an intact skull using wavefront shaping

TL;DR: In vitro demonstration of spatiotemporal regulation of cellular activities by activating photoactivatable proteins through intact skull layer using wavefront shaping is presented.
Posted ContentDOI

High-fidelity optical diffraction tomography of live organisms using non-toxic tunable refractive index media

TL;DR: In this paper , a method for high-fidelity ODT by introducing non-toxic matching media is presented, which optimally reduces the RI contrast and enables visualization of the morphology and intra-organization of live biological samples without producing toxic effects.
Proceedings ArticleDOI

Rapid identification of individual bacterial pathogens using three-dimensional quantitative phase imaging and artificial neural network (Conference Presentation)

TL;DR: In this paper , a hybrid framework of quantitative phase imaging and artificial neural network was proposed to facilitate rapid identification of infectious pathogens at an individual-cell level, where three-dimensional images of refractive index were acquired for individual bacteria, and an optimized artificial neural networks determined the species based on the three-dimensions morphologies, securing 82.5% blind test accuracy at individual cell level.