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.
Second year Ph.D. student @KAIST bio-imaging signal processing & learning lab (BISPL). Currently working as a research intern at the Los Alamos National Laboratory (LANL) applied math and plasma physics division (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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MS in Bio & Brain Engineering, 2021
Korea Advanced Institute of Science & Technology (KAIST)
BS in Biomedical Engineering, 2019
Korea University
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
Resolution of STEM-EDX tomography is greatly enhanced through unsupervised deep learning. We verified the method by comparing the optical properties.