Abstract：

The talk will outline recent approaches for using (deep) convolutional neural networks to solve a wide range of large-scale inverse problems, such as tomographic image reconstruction. Emphasis is on using neural networks for reconstruction that are domain adapted in the sense that they include 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.

Biography：

Prof. Öktem is a chaired professor of mathematics at KTH Royal Institute of Technology in Stockholm, with over 13 years of experience working as an applied mathematician in industry before returning to academia in 2009. His research focuses on theory and algorithms for severely ill-posed inverse problems in tomographic imaging. The work lies at the intersection of mathematical analysis, differential geometry, statistics, and machine learning. Since 2016, he has led work in combining model-based approaches with deep neural networks for uncertainty quantification and task-adapted reconstruction in large-scale inverse problems, driven by applications such as cryo-EM, fluorescence microscopy, low-dose CT and PET/CT, X-ray phase-contrast tomography, and seismic tomography.