Welcome to my personal homepage. I am an ongoing Ph.D. student @KAIST. The motivation of my research is “Making invisible, visible”. Mostly, I work on solving inverse problems arising in computational imaging, and I focus on using deep learning, especially unsupervised learning methods to accomplish it. Unlike many works in literature that simply adopt published methods off-the-shelf, I also try to delve on theoretical links of machine learning and signal processing.
Hyungjin Chung is a Ph.D. student @KAIST bio-imaging signal processing & learning lab (BISPL). His research interests include, but is not restricted to solving inverse problems via deep learning. He has published papers and ongoing projects in a wide variety of fields including compressed sensing MRI (CS-MRI), tomographic reconstruction, microscopy, and phase retrieval.
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MS in Bio & Brain Engineering, 2021
Korea Advanced Institute of Science & Technology (KAIST)
BS in Biomedical Engineering, 2019
Interview at BRIC for the publication of “Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data” in Medical Image Analysis, 2021.
Come-close to diffuse-fast when solving inverse problems with diffusion models. We establish state-of-the-art results with only 20 diffusion steps across various tasks including SR, inpainting, and CS-MRI
Score-based diffusion models beat supervised learning methods on MRI reconstruction.
Resolution of STEM-EDX tomography is greatly enhanced through unsupervised deep learning. We verified the method by comparing the optical properties.
SliceGAN, stochastic model to synthesize 3D microstructures from 2D images, is endowed with the ability to disentangle features, and continuously control these features via AdaIN.
Unsupervised deep learning for simultaneous super-resolution and motion artifact removal of diffusion-weighted MRI scans is proposed.
Two-stage unsupervised reconstruction method for 3D TOF-MRA is developed. A novel projection discriminator in the axial reconstruction step drastically enhances the vessel visiblity.
BarbellNet, which consists of long stack of residual channel attention block(RCAB) was proposed for the reconstruction of fast MRI reconstruction. Reconstruction results through this model was placed 6th in the NeurIPS2020 fastMRI challenge.