{"id":95698,"date":"2024-03-13T11:19:42","date_gmt":"2024-03-13T16:19:42","guid":{"rendered":"https:\/\/engineering.wisc.edu\/?post_type=tribe_events&p=95698"},"modified":"2024-03-19T13:28:42","modified_gmt":"2024-03-19T18:28:42","slug":"ece-rising-stars-seminar-ulugbek-kamilov","status":"publish","type":"tribe_events","link":"https:\/\/engineering.wisc.edu\/event\/ece-rising-stars-seminar-ulugbek-kamilov\/","title":{"rendered":"ECE Machine Learning in Medical Imaging Seminar: Ulugbek Kamilov"},"content":{"rendered":"
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\n\t\t\n\t\t\tMarch 21, 2024\t\t<\/span>\n\n\t\t\t\t\t\n\t\t\t\t @ \t\t\t<\/span>\n\t\t\t\n\t\t\t\t1:30 PM\t\t\t<\/span>\n\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t – \t\t\t\t<\/span>\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t2:30 PM\t\t\t\t<\/span>\n\t\t\t\n\t\t\t\t\t\t<\/h2>\n<\/div>\n\n\n\n

Orchard View Room – Wisconsin Institute for Discovery<\/p>\n\n\n\n

Computational Biomedical Imaging: Restoration Deep Networks as Implicit Priors<\/h2>\n\n\n\n
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Abstract:<\/strong> Many interesting computational imaging problems can be formulated as imaging inverse problems. Since these problems are often ill-posed, one needs to integrate all the available prior knowledge for obtaining high-quality solutions. This talk focuses on the class of methods based on using \u201cimage restoration\u201d deep neural network as data-driven implicit priors on images. The roots of the methods discussed in this talk can be traced to the popular plug-and-play (PnP) family of methods for solving inverse problems. The talk will present the theoretical foundations behind using restoration deep networks as implicit priors as well as applications to image generation in limited angle computed tomography, recovery of continuously represented microscopy images, and solving blind inverse problems in magnetic resonance imaging.<\/p>\n\n\n\n

\"Ulugbek
Ulugbek Kamilov<\/figcaption><\/figure>\n\n\n\n

Biography:<\/strong> Ulugbek Kamilov is the Director of Computational Imaging Group and an Associate Professor of Electrical & Systems Engineering and Computer Science & Engineering at Washington University in St. Louis. He is currently a Visiting Professor at the Data Science Center at \u00c9cole Normale Sup\u00e9rieure in Paris. He obtained the BSc\/MSc degree in Communication Systems in 2011 and the PhD degree in Electrical Engineering in 2015 from EPFL. He was a Visiting Research Faculty at Google Research in 2023-2024 and a Research Scientist at Mitsubishi Electric Research Laboratories in 2015-2017. He was an Exchange Student at Carnegie Mello University in 2008, Visiting Student Researcher at MIT in 2011, and Visiting Scholar at Stanford University in 2013.

He is a recipient of the NSF CAREER Award and the IEEE Signal Processing Society\u2019s 2017 Best Paper Award. He was among 55 early-career researchers in the USA selected as a Fellow for the Scialog initiative on \u201cAdvancing Bioimaging\u201d in 2021. His PhD thesis was selected as a finalist for the EPFL Doctorate Award in 2016. He was awarded Outstanding Teaching Award from the Department of Electrical & Systems Engineering at WashU in 2023. He is currently a Senior Member of the Editorial Board of IEEE Signal Processing Magazine and is on IEEE Signal Processing Society\u2019s Bioimaging and Signal Processing Technical Committee. He has previously served as an Associate Editor of IEEE Transactions on Computational Imaging and on IEEE Signal Processing Society\u2019s Computational Imaging Technical Committee.<\/p>\n\n\n

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Orchard View Room – Third Floor – Discovery Building<\/a><\/h3>\n\t<\/div>\n\n\t\n\t\t\t
\n\t\t\t\n\n330 N. Orchard St.<\/span>\n\t\n\t\t
\n\t\tMadison<\/span>,<\/span>\n\n\n\t53715<\/span>\n\n\n<\/span>\n\n\t\t\t\t\t<\/address>\n\t\n\t\n\t\n\t<\/div>\n\t\n\t<\/div>\n\n\n\n\n\t
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