Ozan Öktem - Bayesian inversion for tomography through machine learning
| Dates: | 10 December 2019 |
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| Times: | 12:00 - 13:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Mathematics |
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| Who is it for: | University staff, External researchers, Current University students |
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| Speaker: | Dr Ozan Öktem |
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Join us for this research seminar, part of the SQUIDS (Statistics, quantification of uncertainty, inverse problems and data science) seminar series.
Abstract: The talk will outline recent approaches for using (deep) convolutional neural networks to solve a wide range of inverse problems, such as tomographic image reconstruction. Emphasis is on learned iterative schemes that use a neural network architecture for reconstruction that includes physics based models for how data is generated. The talk will also discuss recent developments in using generative adversarial networks for uncertainty quantification in inverse problems.
Speaker Bio: Dr. Öktem is an Associate Professor in Mathematics at the Department of Mathematics, KTH - Royal Institute of Technology, Stockholm. He specialises in theory and algorithms for solving severely ill-posed inverse problems with emphasis on tomographic imaging. Prior to joining his current affiliation in 2008, he worked as an applied mathematician for more than 13 years in industry. His research combines methods from mathematical analysis, differential geometry, and mathematical statistics with techniques from machine learning. Focus lately has been on combining model based approaches with deep neural networks for uncertainty quantification and task adapted reconstruction in large scale inverse problems. The research is spearheaded by concrete challenges in imaging applications from various scientific fields, like 3D electron and fluorescence microscopy in bioimaging, low-dose clinical CT and spatiotemporal PET/CT, x-ray phase contrast tomography for bioimaging and material sciences, and lately seismic tomography for geophysical prospecting and single particle cryo-EM.
Speaker
Dr Ozan Öktem
Organisation: KTH Royal Institute of Technology
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