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Mathematical underpinnings of emerging computational imaging inverse problems

Shirin Jalali – Assistant Professor, Rutgers University

Thu, 25-May-2023 / 4:00pm / Packard 202

Abstract

Advanced computational imaging techniques have revolutionized modern imaging systems, surpassing the limitations of conventional methods and enabling diverse imaging capabilities. These systems find widespread applications in fields such as medical diagnosis, astronomy, and agriculture. Designing efficient solutions for these imaging systems involves a multifaceted approach, encompassing mathematical modeling, information theory, signal processing, machine learning, and skillful engineering. In this talk, I will delve into two specific types of imaging systems: i) snapshot compressed sensing, and ii) compressive coherence-based imaging in the presence of speckle noise. By exploring these systems, I will highlight their unique challenges and the opportunities they present. Furthermore, I will discuss our progress in these areas, focusing on the development of fundamental mathematical models and algorithmic solutions inspired by theory. Additionally, I will examine a wide range of challenges and open problems that need to be addressed before these solutions can become fully practical, emphasizing the crucial steps necessary to realize the full potential of these innovative imaging systems.

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.