Shirin Jalali

Biography: 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.

Research overview:  My primary background is in information theory, statistical signal processing, and machine learning. Currently, the key focus of my research group is on the following problems, which have a wide range of applications:

1. Developing theoretically-founded, computationally-efficient algorithms for modern computational imaging inverse problems, such as coherent imaging in the presence of speckle noise and snapshot compressive imaging. 

2. Developing a novel information-theoretic approach to the problem of structure learning and applying it to solve a range of inference problems. 

Selected recent publications:

• Mengyu Zhao, Xi Chen, Xin Yuan, Shirin Jalali, "Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms", 2024 arxiv

• Xi Chen, Zhewen Hou , Christopher Metzler, Arian Maleki, Shirin Jalali, "Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise", ICML 2024, arxiv

• Xi Chen, Zhewen Hou , Christopher Metzler, Arian Maleki, Shirin Jalali, "Multilook compressive sensing in the presence of speckle noise", NeurIPS 2023 Workshop on Deep Learning and Inverse Problems,

• Ziyi Meng, Xin Yuan, Shirin Jalali, "Deep unfolding for snapshot compressive imaging", International Journal of Computer Vision, 2023

• Mengyu Zhao, Shirin Jalali, Theoretical analysis of binary masks in snapshot compressive imaging systems, Allerton 2023, arxiv