Jun
03
Date: 03-June-2026
Time: 9:30 AM ET (New York Time)
Duration: Approximately 90 minutes
Presenter: Dr. Ulugbek Kamilov
Abstract
Computational imaging problems are often formulated as ill-posed inverse problems, requiring the integration of prior knowledge to recover high-quality images from limited or corrupted measurements. This talk focuses on score-based methods, which approximate the gradient of the log-prior (known as the score function) using pre-trained deep restoration networks. The presenter I will present recent advances for learning score functions without access to clean training data. These include characterizing score functions via general restoration networks, learning from partial measurements, and model-based strategies that incorporate forward models into training. The talk will also cover the theoretical foundations of these approaches and their applications in biomedical image reconstruction.
Biography
Ulugbek S. Kamilov received the B.Sc./M.Sc. in communication systems and the Ph.D. in electrical engineering from EPFL, Switzerland in 2011 & 2015 respectively.
He is currently the Leon and Elizabeth Janssen Associate Professor of Electrical and Computer Engineering (ECE) at the University of Wisconsin–Madison, where he founded and leads the Computational Imaging Group (CIG). Prior to joining UW–Madison, he held academic and research appointments as the Donald L. Snyder Associate Professor at Washington University in St. Louis, Visiting Professor at École Normale Supérieure in Paris, Visiting Faculty Researcher at Google, and Research Scientist at Mitsubishi Electric Research Laboratories (MERL).
Dr. Kamilov is a recipient of the IEEE Signal Processing Society’s 2024 Pierre-Simon Laplace Early Career Technical Achievement Award, the IEEE Signal Processing Society 2017 Best Paper Award, and the NSF CAREER Award. He was named a Scialog Fellow for Advancing Bioimaging in 2021 and was a finalist for the EPFL Doctorate Award in 2016. In recognition of his teaching, he received the Outstanding Teaching Award from WashU’s Department of Electrical & Systems Engineering in 2023. He currently serves on the IEEE Signal Processing Society’s Computational Imaging Technical Committee, and has previously served as a Senior Editorial Board Member of IEEE Signal Processing Magazine and Associate Editor of IEEE Transactions on Computational Imaging.
