Domain expansion via network adaptation for solving inverse problems
IEEE Transactions on Computational Imaging, 2024
A method for adapting neural networks to solve inverse problems under domain shift.
arXiv: arXiv | Project Website: Project Website
Posts by Collection
publications
Parameter-efficient adaptation for computational imaging
ICASSP 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024
A parameter-efficient adaptation framework for computational imaging tasks.
Gaussian is all you need: A unified framework for solving inverse problems via diffusion posterior sampling
IEEE Transactions on Computational Imaging, 2025
A unified diffusion posterior sampling framework for inverse problems.
Covariance-corrected diffusion models for solving inverse problems
2025 IEEE Statistical Signal Processing Workshop (SSP), 2025
A covariance-corrected diffusion model approach for inverse problems.
Parameter-efficient multi-task and multi-domain learning using factorized tensor networks
IEEE Open Journal of Signal Processing, 2025
A factorized tensor network approach for parameter-efficient multi-task and multi-domain learning.
Provable and robust wavefront sensing via self-reference interferometry
arXiv preprint arXiv:2604.03564, 2026
A robust self-reference interferometry framework for wavefront sensing.
arXiv: arXiv
teaching
Teaching experience 1 Permalink
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2 Permalink
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.