**Abstract**: In this talk, I will focus on two challenging imaging systems: snapshot compressive imaging and coherent imaging under speckle noise interference. I will begin by reviewing the core mathematical modeling of the inverse problem corresponding to each system. I will develop a maximum likelihood estimator (MLE)-based optimization for each, employing untrained neural networks (NNs) to model the source structure. Theoretical analysis of the MLE-based methods will be shown to enable, on one hand, an understanding of the fundamental limits of these systems and, on the other hand, optimization of the image recovery algorithms and hardware. I will also discuss our proposed algorithms that merge classic bagging ideas with untrained neural networks for solving the inverse problems in these imaging systems. For each application, I will demonstrate how our method achieves state-of-the-art performance.

**Bio**: Shirin Jalali is an Assistant Professor at the ECE department at Rutgers University. Prior to joining Rutgers in 2022, she was a research scientist at the AI Lab at Nokia Bell Labs. She has also held positions as a Research Scholar at Princeton University and as a Faculty Fellow at NYU Tandon School of Engineering. She obtained her M.Sc. in Statistics and Ph.D. in Electrical Engineering from Stanford University. She has been serving as an Associate Editor of IEEE Transactions on Information Theory since 2021 and is a recipient of 2023 NSF CAREER award. Her research interests primarily lie in information theory, statistical signal processing, and machine learning. She applies these disciplines to tackle computational imaging inverse problems and explore the fundamental limits of structure learning.