Zach Stoebner

:: about me

Hi, I’m Zach.

I’m an Electrical & Computer Engineering PhD student at UT Austin working with Prof. Jon Tamir in the Computational Sensing & Imaging Lab.

My research interests span computational imaging & computer vision and machine learning & optimization. Currently, I am working on provable learning methods for solving inverse problems in Fourier imaging systems with an emphasis on magnetic resonance imaging. Generally, I’m curious about AI/ML & signal processing, optimization & control, and intelligent systems & robotics.

Check out my notes to see what I’m thinking about and to see what I’m seeing!

I’m always open to new opportunities and collaborations within my interests. You can contact me at zstoebner@austin.utexas.edu.

CV GitHub LinkedIn Scholar

:: research

in progress

“Generalized system identification with implicit neural representations while jointly reconstructing the image in MRI”, Zachary A. Stoebner, Jonathan I. Tamir.

“Preconditioned monotone operator learning for fast, memory-efficient, noise-robust compressed sensing MRI”, Zachary A. Stoebner, Jonathan I. Tamir.

conference

“INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstruction”, Yamin Arefeen, Brett Levac, Zach Stoebner, Jonathan I Tamir. Asilomar . (2024, accepted) arXiv

“Segmentation of kidney stones in endoscopic video feeds”, Zachary A. Stoebner, Daiwei Lu, Seok Hee Hong, Nicholas L. Kavoussi, and Ipek Oguz, Proc. SPIE 12032, Medical Imaging 2022: Image Processing (2022). DOI arXiv note

journal

“Reducing malware analysis overhead with coverings”, Michael Sandborn, Zach Stoebner, Westley Weimer, Stephanie Forrest, Ryan Dougherty, Jules White, Kevin Leach. IEEE‑TDSC (2023). DOI repo

“Comprehensive shape analysis of the cortex in Huntington’s disease”, Zachary A. Stoebner, Kilian Hett, Ilwoo Lyu, Hans Johnson, Jane S. Paulsen, Jeffrey Long, Ipek Oguz, Human Brain Mapping (2023). DOI repo note

:: news