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.
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).
Download my CV.
MS in Bio & Brain Engineering, 2021
Korea Advanced Institute of Science & Technology (KAIST)
BS in Biomedical Engineering, 2019
Diffusion posterior sampling enables solving arbitrary noisy (e.g. Gaussian, Poisson) inverse problems that are both linear or non-linear.
We propose a method to perform posterior sampling with diffusion models on blind inverse problems.
We propose a method that can solve 3D inverse problems in the medical imaging domain using only the pre-trained 2D diffusion model augmented with the conventional model-based prior.
Manifold constraint dramatically improves the performance of unsupervised inverse problem solving using diffusion models.
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
Score-based diffusion models beat supervised learning methods on MRI reconstruction.