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Hyungjin Chung

Research Scientist
EverEx (Mandatory Korean Military Service ~ 2026.02)
hyungjin.chg (at) gmail.com


About Me

I am a Research Scientist at EverEx, where I lead the AI research team together with Byung-Hoon Kim. Before joining EverEx, I did my Ph.D. at KAIST where I was advised by Jong Chul Ye. During my Ph.D., I also spent my time as a research intern at NVIDIA Research, Google Research, and Los Alamos National Laboratory. I pioneered and advanced some of the most widely acknowledged works on diffusion model-based inverse problem solvers. I’m interested broadly in the intersection of generative models, imaging, multimodal representations, and their applications to real-world problems.

I am looking for highly motivated researchers to work with me at EverEx for pushing the boundaries of video understanding using LLMs. If you are interested, send me an email with your CV attached.

News

Publications

  1. CVPR
    Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI
    Won Jun Kim*, Hyungjin Chung*, Jaemin Kim*, Sangmin Lee, Byeongsu Sim, Jong Chul Ye
    IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025.
    PDF

  2. ICLR
    CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models
    Hyungjin Chung*, Jeongsol Kim*, Geon Yeong Park*, Hyelin Nam*, Jong Chul Ye
    International Conference on Learning Representations, 2025.
    PDF Code Project Page Silver prize, 31st Samsung Humantech Paper Award

  3. ICLR
    Regularization by texts for latent diffusion inverse solvers
    Jeongsol Kim*, Geon Yeong Park*, Hyungjin Chung, Jong Chul Ye
    International Conference on Learning Representations, 2025.
    PDF Code Project Page Spotlight presentation

  4. IEEE TMI
    Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
    Riccardo Barbano*, Alexander Denker*, Hyungjin Chung*, Tae Hoon Roh, Simon Arrdige, Peter Maass, Bangti Jin, Jong Chul Ye
    IEEE Transactions on Medical Imaging, 2025.
    PDF Code

  5. IEEE JBHI
    Fundus image enhancement through direct diffusion bridges
    Sehui Kim*, Hyungjin Chung*, Se Hie Park, Eui-Sang Chung, Kayoung Yi, Jong Chul Ye
    IEEE Journal of Biomedical and Health Informatics, 2024.
    PDF Code

  6. ECCV
    Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems
    Hyungjin Chung, Jong Chul Ye
    European Conference on Computer Vision, 2024.
    PDF Code Gold prize, 30th Samsung Humantech Paper Award

  7. ICML
    Prompt-tuning Latent Diffusion Models for Inverse Problems
    Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio
    International Conference on Machine Learning, 2024.
    PDF

  8. ICLR
    Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
    Hyungjin Chung, Suhyeon Lee, Jong Chul Ye
    International Conference on Learning Representations, 2024.
    PDF Code

  9. ICCV
    Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models
    Suhyeon Lee*, Hyungjin Chung*, Minyoung Park, Jonghyuk Park, Wi-Sun Ryu, Jong Chul Ye
    IEEE/CVF International Conference on Computer Vision, 2023.
    PDF Code

  10. CVPR
    Parallel Diffusion Models of Operator and Image for Blind Inverse Problems
    Hyungjin Chung*, Jeongsol Kim*, Sehui Kim, Jong Chul Ye
    IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
    PDF Code Project Page Video

  11. CVPR
    Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models
    Hyungjin Chung*, Dohoon Ryu*, Michael T. Mccann, Marc L. Klasky, Jong Chul Ye
    IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
    PDF Code Video

  12. ICLR
    Diffusion Posterior Sampling for General Noisy Inverse Problems
    Hyungjin Chung*, Jeongsol Kim*, Michael T. Mccann, Marc L. Klasky, Jong Chul Ye
    International Conference on Learning Representations, 2023.
    PDF Code Project Page Spotlight; Gold prize, 29th Samsung Humantech Paper Award

  13. NeurIPS
    Improving Diffusion Models for Inverse Problems using Manifold Constraints
    Hyungjin Chung*, Byeongsu Sim*, Dohoon Ryu, Jong Chul Ye
    Advances in Neural Information Processing Systems, 2022.
    PDF Code

  14. CVPR
    Come-Closer-Diffuse-Faster Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction
    Hyungjin Chung, Byeongsu Sim, Jong Chul Ye
    IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
    PDF

  15. IEEE TMI
    MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion
    Hyungjin Chung, Eun Sun Lee, Jong Chul Ye
    IEEE Transactions on Medical Imaging, 2022.
    PDF

  16. MeDIA
    Score-based diffusion models for accelerated MRI
    Hyungjin Chung, Jong Chul Ye
    Medical Image Analysis, 2022.
    PDF Code

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

  18. Deep Learning Model for Diagnosing Gastric Mucosal Lesions Using Endoscopic Images: Development, Validation, and Method Comparison
    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
    Gastrointestinal Endoscopy, 2021.
    PDF

  19. Nat. Mach. Intell.
    Deep learning STEM-EDX tomography of nanocrystals
    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
    Nature Machine Intelligence, 2021.
    PDF Code 2021 March Issue Cover

  20. Nat. Mach. Intell.
    Reusability report: Feature disentanglement in generating a three-dimensional structure from a two-dimensional slice with sliceGAN
    Hyungjin Chung, Jong Chul Ye
    Nature Machine Intelligence, 2021.
    PDF Code

  21. Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement
    Mehmet Akcakaya, Burhaneddin Yaman, Hyungjin Chung, Jong Chul Ye
    IEEE Signal Processing Magazine, 2021.
    PDF

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

  23. IEEE TCI
    Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain
    Hyungjin Chung, Jaeyoung Huh, Geon Kim, Yong Keun Park, Jong Chul Ye
    IEEE Transactions on Computational Imaging, 2020.
    PDF

  24. Unpaired training of deep learning tMRA for flexible spatio-temporal resolution
    Gyutaek Oh, Byeongsu Sim, Hyungjin Chung, Leonard Sunwoo, Jong Chul Ye
    IEEE Transactions on Computational Imaging, 2020.
    PDF

  25. Unpaired training of deep learning tMRA for flexible spatio-temporal resolution
    Eunju Cha, Hyungjin Chung, Eung Yeop Kim, Jong Chul Ye
    IEEE Transactions on Medical Imaging, 2020.
    PDF


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