Blog - Hyungjin

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 generative modeling, especially diffusion models. I am both interested in generally advancing diffusion model-based inverse problem solvers, and applying them to solve important real-world problems.

Hyungjin Chung

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). Diffusion models and inverse problems enthusiast. Hyungjin Chung has pioneered and advanced some of the most widely acknowledged works on diffusion model-based inverse problem solvers. Interested in 1) Advancing and widening the applicability of diffusion models in inverse imaging, 2) Acceleration of diffusion models, 3) Application to solve real-world problems (e.g. medical imaging).

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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

Recent Publications

(2023). Diffusion Posterior Sampling for General Noisy Inverse Problems. ICLR 2023 (SPOTLIGHT).

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(2022). Parallel Diffusion Models of Operator and Image for Blind Inverse Problems. In Arxiv.

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(2022). Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models. In Arxiv.

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

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

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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 deep learning for accelerated MRI using optimal transport driven cycleGAN. IEEE TCI.

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

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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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