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ECE Colloquium Series – Prof. Ulugbek Kamilov
February 20 @ 3:30 pm - 5:00 pm
As part of the *Eleanore Hale Wilson Lecture Series, ECE is proud to present:
“Computational Imaging: Reconciling Models and Learning”
Prof. Ulugbek Kamilov
Washington University in Saint Louis
Host: Prof. Mehmet Akçakaya
There is a growing need in biological, medical, and materials imaging research to recover information lost during data acquisition. There are currently two distinct viewpoints on addressing such information loss: model‐based and learning‐based. Model‐based methods leverage analytical signal properties (such as wavelet sparsity) and often come with theoretical guarantees and insights. Learning‐based methods leverage flexible representations (such as convolutional neural nets) for best empirical performance through training on big datasets.
The goal of this talk is to introduce a framework that reconciles both viewpoints by providing the “deep learning prior” counterpart of the classical regularized inversion. This is achieved by specifying “artifact‐removing deep neural nets” as a mechanism to infuse learned priors into recovery problems, while maintaining a clear separation between the prior and physics‐based acquisition models. Our methodology can fully leverage the flexibility offered by deep learning by designing learned prior to be used within our new family of fast iterative algorithms. Yet, our results indicate that the such algorithms can achieve state‐of‐the‐art performance in different computational imaging tasks, while also being amenable to rigorous theoretical analysis. We will focus on the application of the methodology to the problem to various biomedical imaging modalities, such as magnetic resonance imaging and intensity diffraction tomography.
Ulugbek S. Kamilov is an Assistant Professor and Director of Computational Imaging Group (CIG) at Washington University in St. Louis. His research area is computational imaging with an emphasis on large‐scale image recovery and automated image analysis for applications such as optical microscopy, magnetic resonance imaging, and tomographic imaging. His research interests include signal and image processing, machine learning, and large‐scale optimization. He obtained his BSc and MSc degrees in Communication Systems, and the PhD degree in Electrical Engineering from EPFL, Switzerland, in 2008, 2011, and 2015, respectively. From 2015 to 2017, he was a Research Scientist at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA. He is a recipient of the IEEE Signal Processing Society’s 2017 Best Paper Award (with V. K. Goyal and S. Rangan). His PhD thesis was selected as a finalist for the EPFL Doctorate Award in 2016. His work on Learning Tomography (LT) was featured in Nature “News and Views” in 2015. He is a member of IEEE Technical Committee on Computational Imaging (2016‐2019, 2019‐present). He is serving as Associate Editor for the IEEE Transactions on Computational Imaging (2019‐present).
*Established in 2009, the Eleanore Hale Wilson Fund supports engineering field leaders for travel to Minnesota to share their expertise and discoveries with University of Minnesota graduate students, faculty, and alumni. The fund also supports the receptions held in honor of each speaker.