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In my free time, I enjoy reading astronomy books, painting, and working as a voice actor for children's audiobooks on YouTube!

Minseo (Sonia) Kim

I am a first year M.S. student in Electrical Engineering at Stanford University working in the Stanford Computational Imaging Lab, advised by Prof. Gordon Wetzstein.

I am broadly interested in developing computational imaging algorithms that tackle complex inverse problems across diverse scientific domains such as medical imaging, optical imaging, astronomical imaging, X-ray crystallography, and cryo-electron microscopy, leveraging AI tools like generative models and physics-informed neural networks to enhance reconstruction. I am passionate about bridging the gap between computational methods and real-world imaging challenges to advance scientific discovery.

I did my B.S.E. (Honors) in Electrical Engineering and Data Science with a minor in Mathematics at University of Michigan - Ann Arbor, where I was advised by Prof. Jeffrey Fessler.


Research Projects

PontTuset Dual Ascent Diffusion for Inverse Problems
Minseo Kim, Axel Levy, Gordon Wetzstein
under review, 2025
project page / bibtex / code (coming soon) / arXiv

A diffusion model-based solver for inverse problems that is better, faster, and more robust to noise than relevant baselines.

PontTuset Machine Learning Models for Undersampled MRI Reconstruction
Minseo Kim, Jeffrey Fessler (capstone advisor)
B.S.E. Honors Capstone Project, 2024
poster / bibtex / code / pdf

I implemented a score-based diffusion model with posterior sampling to reconstruct high-quality MRI images from undersampled k-space data, tested on over 3000 FastMRI dataset images.

PontTuset Interstellar Dust Scattering: Mathematical Modeling and Halo Intensity Calculations
Minseo Kim, Lia Corrales (capstone advisor)
B.S.E. Data Science Capstone Project, 2024
code / pdf

Designed the double interstellar dust scattering physics model and developed mathematical proofs to derive halo intensity using analytic and numerical methods.

PontTuset PyTorch and Julia Software for Image Reconstruction
Minseo Kim, Jeffrey Fessler (advisor)
Software package development, 2023
doc / julia / pytorch

Implemented 2D branchless distance-driven forward projection and backprojection algorithm for computed tomography (CT) reconstruction using both PyTorch and Julia language.


Industry Experiences

Zoox

Research Intern
SLAC National Accelerator Laboratory, MLCV@LCLS
June 2025 -

Research in the area of computational structural biology focused on developing experiment-grounded protein structure generative models, with Doris Mai, Frederic Poitevin, and Prof. Matthias Kling.

Zoox

Digital Signal Processing Intern
Zoox, Inc., Advanced Hardware Engineering Team
May 2024 - August 2024

Worked on developing AI-powered audio mining systems and multimodal data processing pipelines, implementing models like PANN and AudioCLIP for text-based audio search and applying DSP techniques for vehicle data preprocessing, with Venkata Chebiyyam and Shaminda Subasingha.


Others

Talks

Center for Decoding the Universe Annual Conference, 2025
Michigan Student Symposium for Interdisciplinary Statistical Sciences, 2024
SIAM Great Lakes Conference, 2023

Awards

Honorable Mention Award at MSSISS, 2024
William J. Branstrom Freshman Prize, 2021

Teaching

Course Assistant, Applied Honors Calculus II (MATH 156) Fall 2022

Languages

Korean (native)
English (bilingual)
Chinese (bilingual)
Contact me at kminseo@stanford.edu