Preprint posted: Our recent work on estimating physical parameters from radiographic measurements with machine learning “Learning robust parameter inference and density reconstruction in flyer plate impact experiments” has been announced on arXiv.
About Me
I am a PhD student in Electrical and Computer Engineering at Johns Hopkins University, where I am a member of the Hopkins Computational Imaging Group led by Yu Sun. Previously, I was a post-baccalaureate researcher at Los Alamos National Laboratory and Michigan State University, where I worked with the Theoretical Division and Saiprasad Ravishankar’s SLIM Group, respectively. Before that, I graduated from MSU with degrees in Mathematics and Physics.
My current research focuses on using machine learning for solving inverse problems in imaging and physics. I am particularly interested in unsupervised learning and generative modeling for scientific and medical imaging. A list of my publications can be found on my Google Scholar.
News
2025
NSF GRFP: I have been awarded an NSF Graduate Research Fellowship!
DOE CSGF: I am happy to share that I have been awarded a Department of Energy Computational Science Graduate Fellowship to support my research for the next four years!
Preprint posted: Our review paper on using untrained networks to solve inverse problems “Understanding Untrained Deep Models for Inverse Problems: Algorithms and Theory” has been announced on arXiv.
IEEE TCI: My paper “Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction” has been accepted to IEEE Transactions on Computational Imaging!