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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.

Papers
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Proceedings ArticleDOI

Quantitative Phase Imaging of Fluid Mixing in Microfluid Chips

TL;DR: In this article, a method for imaging and quantifying microfluidic mixing using quantitative phase imaging method is presented, which is one of the important topics with a various application for biomedicine, chemical engineering, etc.
Patent

Multifunctional optical element and method using multiple light scattering

TL;DR: In this paper, a multifunctional optical element and method using multiple light scattering was proposed, where the interference pattern on the photorefractive materials was reconstructed by radiating the reference beam to the complex media.
Journal ArticleDOI

Unique Red Blood Cell Morphology Detected in a Patient with Myelodysplastic Syndrome by Three-dimensional Refractive Index Tomography

TL;DR: In this study, optical diffraction tomography (ODT) was employed to perform three-dimensional imaging of RBCs from a patient with MDS to provide information about the morphological, biochemical, and mechanical parameters at the individual cell level.
Posted ContentDOI

Real-time monitoring of bacterial growth and fast antimicrobial susceptibility tests exploiting multiple light scattering

TL;DR: The distinctive responses of several species to microbial agents were revealed through the present technique supporting a comprehensive analysis of the effect of the antibiotics, and it is suggested that this new method could be a useful tool for rapid, simple, and low-cost ASTs.
Posted Content

Unsupervised Missing Cone Deep Learning in Optical Diffraction Tomography.

TL;DR: In this article, an unsupervised deep learning framework was proposed to learn the probability distribution of missing projection views through an optimal transport driven cycleGAN for optical diffraction tomography (ODT).