Blog - Hyungjin

📚 Welcome to my Homepage

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

Hyungjin Chung

Ph.D. student - Deep Learning & Inverse Problems

Korea Advanced Institute of Science and Technology (KAIST) BISPL


Second year Ph.D. student @KAIST bio-imaging signal processing & learning lab (BISPL). Currently working as a research intern at the Los Alamos National Laboratory (LANL) applied math and plasma physics division (T-5). His research interests include, but is not restricted to solving inverse problems via generative modeling, and especially diffusion models. 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.

Download my CV.

  • Deep Learning
  • Diffusion Model
  • Energy-based Model
  • Computational Imaging
  • Inverse Problems
  • MS in Bio & Brain Engineering, 2021

    Korea Advanced Institute of Science & Technology (KAIST)

  • BS in Biomedical Engineering, 2019

    Korea University

Recent Posts

Recent Publications

(2022). Low-dose sparse-view HAADF-STEM-EDX tomography of nanocrystals using unsupervised deep learning. In ACS Nano.


(2022). Improving Diffusion Models for Inverse Problems using Manifold Constraints. In ArXiv.

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(2021). Deep Learning Model for Diagnosing Gastric Mucosal Lesions Using Endoscopic Images: Development, Validation, and Method Comparison. GIE.

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(2021). Score-based diffusion models for accelerated MRI. In MedIA.

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(2021). Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement. IEEE SPM.

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(2021). Deep learning STEM-EDX tomography of nanocrystals. In Nat. Mach. Int..

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(2020). Unpaired training of deep learning tMRA for flexible spatio-temporal resolution. IEEE TMI.

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(2020). Unpaired deep learning for accelerated MRI using optimal transport driven cycleGAN. IEEE TCI.

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(2020). Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning. In ArXiv.

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(2020). Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data. In Medical Image Analysis.

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(2020). Deep Learning Fast MRI Using Channel Attention in Magnitude Domain. IEEE ISBI.

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(2020). Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain. In IEEE TCI.

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