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

Ph.D. student - Deep Learning & Inverse Problems

Korea Advanced Institute of Science and Technology (KAIST) BISPL

Biography

Second year Ph.D. student @KAIST bio-imaging signal processing & learning lab (BISPL). Prior research intern at the Los Alamos National Laboratory (LANL) applied math and plasma physics group (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.

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Interests

Deep Learning
Diffusion Model
Energy-based Model
Computational Imaging
Inverse Problems

Education

MS in Bio & Brain Engineering, 2021
Korea Advanced Institute of Science & Technology (KAIST)
BS in Biomedical Engineering, 2019
Korea University

Projects

Probabilistic Machine Learning - Kevin Murphy

Brief summary, guidance, and solutions to the famous machine-learning bible “Probabilistic Machine Learning” in Korean.

News

Hyungjin Chung

Apr 27, 2021 12:00 AM

Interview at BRIC

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.

Hyungjin Chung

Mar 21, 2021 12:00 AM

Nature Machine Intelligence Cover selected

The paper I have co-authored, link is featured as the cover of the March issue in Nature Machine Intelligence

Featured Publications

Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, Jong Chul Ye

September 2022 In NeurIPS 2022

Improving Diffusion Models for Inverse Problems using Manifold Constraints

Manifold constraint dramatically improves the performance of unsupervised inverse problem solving using diffusion models.

Hyungjin Chung, Byeongsu Sim, Jong Chul Ye

March 2022 In CVPR 2022

Come-Closer-Diffuse-Faster Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction

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

Hyungjin Chung, Jong Chul Ye

October 2021 In MedIA

Score-based diffusion models for accelerated MRI

Score-based diffusion models beat supervised learning methods on MRI reconstruction.

Yoseob Han, Jaeduck Jang, Eunju Cha, Junho Lee, Hyungjin Chung, Myoungho Jeong, Tae-Gon Kim, Byeong Gyu Chae, Hee Goo Kim, Shinae Jun, Sungwoo Hwang, Eunha Lee, Jong Chul Ye

February 2021 In Nat. Mach. Int.

Deep learning STEM-EDX tomography of nanocrystals

Resolution of STEM-EDX tomography is greatly enhanced through unsupervised deep learning. We verified the method by comparing the optical properties.

Recent Publications

Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, Jong Chul Ye (2022). Improving Diffusion Models for Inverse Problems using Manifold Constraints. In NeurIPS 2022.

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Eunju Cha, Hyungjin Chung, Jaeduck Jang, Junho Lee, Eunha Lee, Jong Chul Ye (2022). Low-dose sparse-view HAADF-STEM-EDX tomography of nanocrystals using unsupervised deep learning. In ACS Nano.

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Hyungjin Chung, Byeongsu Sim, Jong Chul Ye (2022). Come-Closer-Diffuse-Faster Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction. In CVPR 2022.

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Joon Yeul Nam, Hyungjin Chung, Kyu Sung Choi, Hyuk Lee, Seung Jun Han, Tae Jun Kim, Hosim Soh, Eun Kang, Soo_jeong Cho, Jong Chul Ye, Jong Pil Im, Sang Gyun Kim, Yoon Jun Kim, Joo Sung Kim, Jung-Hwan Yoo, Hyunsoo Chung, Jeong-Hoon Lee (2021). Deep Learning Model for Diagnosing Gastric Mucosal Lesions Using Endoscopic Images: Development, Validation, and Method Comparison. GIE.

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Hyungjin Chung, Jong Chul Ye (2021). Score-based diffusion models for accelerated MRI. In MedIA.

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Mehmet Akcakaya, Burhaneddin Yaman, Hyungjin Chung, Jong Chul Ye (2021). Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement. IEEE SPM.

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Yoseob Han, Jaeduck Jang, Eunju Cha, Junho Lee, Hyungjin Chung, Myoungho Jeong, Tae-Gon Kim, Byeong Gyu Chae, Hee Goo Kim, Shinae Jun, Sungwoo Hwang, Eunha Lee, Jong Chul Ye (2021). Deep learning STEM-EDX tomography of nanocrystals. In Nat. Mach. Int..

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Eunju Cha, Hyungjin Chung, Eung Yeop Kim, Jong Chul Ye (2020). Unpaired training of deep learning tMRA for flexible spatio-temporal resolution. IEEE TMI.

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Gyutaek Oh, Byeongsu Sim, Hyungjin Chung, Leonard Sunwoo, Jong Chul Ye (2020). Unpaired deep learning for accelerated MRI using optimal transport driven cycleGAN. IEEE TCI.

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Hyungjin Chung, Jong Chul Ye (2020). Feature Disentanglement in generating three-dimensional structure from two-dimensional slice with sliceGAN. In Nat. Mach. Int..

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Hyungjin Chung, Jaehyun Kim, Jeong Hee Yoon, Jeong Min Lee, Jong Chul Ye (2020). Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning. In ArXiv.

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Hyungjin Chung, Eunju Cha, Leonard Sunwoo, Jong Chul Ye (2020). Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data. In Medical Image Analysis.

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Joonhyung Lee, Hyunjong Kim, Hyungjin Chung, Jong Chul Ye (2020). Deep Learning Fast MRI Using Channel Attention in Magnitude Domain. IEEE ISBI.