Blog - Hyungjin

Hyungjin Chung

Hyungjin Chung

Ph.D. student - Generative Models & Inverse Problems

Korea Advanced Institute of Science and Technology (KAIST) BISPL

Biography

Final year Ph.D. student @KAIST bio-imaging signal processing & learning lab (BISPL). Prior research intern at NVIDIA Research, Google Research Perception (LUMA), and 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. My research interests include, but are not restricted to advancing and widening the applicability of diffusion models in inverse imaging, applications to solve real-world problems (e.g. medical imaging).

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Interests
  • Deep Learning
  • Diffusion Model
  • Computational Imaging
  • Inverse Problems
Education
  • PhD in Bio & Brain Engineering, Current

    KAIST

  • MS in Bio & Brain Engineering, 2021

    KAIST

  • BS in Biomedical Engineering, 2019

    Korea University

Recent Publications

(2024). Prompt-tuning Latent Diffusion Models for Inverse Problems. ICML 2024.

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(2024). Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems. ICLR 2024.

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(2023). Direct Diffusion Bridge for Inverse Problems with Data Consistency. In NeurIPS 2023.

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(2023). Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models. In ICCV 2023.

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(2023). Parallel Diffusion Models of Operator and Image for Blind Inverse Problems. In CVPR 2023.

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(2023). Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models. In CVPR 2023.

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

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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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