abstract
Synthetic Aperture Radar (SAR) is a radar imaging mode that maps radar reflectivity of the ground. This is an important earth resource monitoring and analysis tool in the civilian and government communities, and an important intelligence, surveillance, and reconnaissance (ISR) tool for the military and intelligence communities. The tutorial proposed herein is intended to provide an introduction to the physical concepts, processing, performance, features, and exploitation modes that make SAR work, and make it useful. Although mathematics will be shown in some parts of the presentation, the lecture will focus on the qualitative significance of the mathematics rather than dry derivations. Liberal use of example SAR images and other data products will be used to illustrate the concepts discussed. The presentation will be given as fourdistinct modules, each based on (but enhanced from) presentations developed and given by the presenter in numerous non-public forums to government, military, industry, and academic groups.
Presented by armin doerry (SANDIA NATIONAL LABS)
biography
Dr. Armin Doerry is a Distinguished Member of Technical Staff in the Intelligence, Surveillance and Reconnaissance (ISR) Mission Engineering Department of Sandia National Laboratories. He holds a Ph.D. in Electrical Engineering from the University of New Mexico. He has worked in numerous aspects of Synthetic Aperture Radar and other radar systems’ analysis, design, and fabrication since 1987, and continues to do so today. He has taught Radar Signal Processing classes (and related topics) as an adjunct professor at theUniversity of New Mexico, and has taught numerous seminars on SAR and other radar topics togovernment, military, industry, and academic groups.
Presented by liyue shen (university of michigan, ann arbor)
BIOGRAPHY
Liyue Shen is an Assistant Professor in the EECS department at the University of Michigan. Prior to that, she received her B.E. degree in Electronic Engineering from Tsinghua University in 2016, and obtained her Ph.D. degree from the Department of Electrical Engineering, Stanford University in 2022. She also spent one year as a postdoctoral research fellow at the Department of Biomedical Informatics, Harvard Medical School. Her research interest is in Biomedical AI, which lies in the interdisciplinary areas of machine learning, computer vision, signal and image processing, biomedical imaging, medical image analysis, and data science. She recently focuses on the generative diffusion models, implicit neural representation learning and multimodal foundation models. She is the recipient of Stanford Bio-X Bowes Graduate Student Fellowship (2019-2022), and was selected as the Rising Star in EECS by MIT and the Rising Star in Data Science by the University of Chicago in 2021. She serves as area chairs for ICLR, ICML, MLHC, and helps organize multiple conferences and workshops including CPAL, ISBI, WiML, ML4H. Website: https://liyueshen.engin.umich.edu/
and QING QU (UNIVERSITY OF MICHIGAN, ANN ARBOR)
biography
Qing Qu is an Assistant Professor in Electrical Engineering and Computer Science at the University of Michigan. He works at the intersection of the foundations of machine learning, numerical optimization, and signal/image processing, with a current focus on the theory of deep generative models and representation learning. Prior to joining Michigan in 2021, he was a Moore–Sloan Data Science Fellow at the Center for Data Science, New York University (2018–2020). He received his Ph.D. in Electrical Engineering from Columbia University in October 2018 and his B.Eng. in Electrical and Computer Engineering from Tsinghua University in July 2011. His work has been recognized with multiple honors, including the Best Student Paper Award at SPARS 2015, a Microsoft PhD Fellowship in Machine Learning (2016), the Best Paper Award at the NeurIPS Diffusion Models Workshop (2023), an NSF CAREER Award (2022), an Amazon Research Award (AWS AI, 2023), a UM CHS Junior Faculty Award (2025), and a Google Research Scholar Award (2025). He was one of the founding organizers and Program Chair for the new Conference on Parsimony & Learning (CPAL), regularly serves as an Area Chair for NeurIPS, ICML, and ICLR, and is an Action Editor for TMLR.
abstract
Coming soon!
Presented by Shubham sharma (galaxeye space)
biography
Shubham Sharma is a Senior Member of IEEE with nearly a decade of experience in SAR image processing. He serves as Secretary of the IEEE Synthetic Aperture Standards Committee and as Vice Chair of the Recommended Practice for Leveraging Machine Learning in Synthetic Aperture Imaging and Sensing working group, contributing to both initiatives and collaborative efforts at the intersection of IEEE GRSS and SASC. He holds a Master’s degree in Computer Science and Engineering with a specialization in Networking Technologies from Nirma University, Ahmedabad, India, and his past professional experience includes fellowship projects with ISRO, where he worked on processing data from RISAT-1 as well as other major spaceborne SAR sensors. Shubham operates at the confluence of Remote Sensing and Computer Vision and has contributed to conferences focused on scientific programming and image processing with Python, including SciPy and PyCon. His research interests span SAR signal processing, image processing, computer vision, and remote sensing.