Machine Learning Solutions for Inverse Problems: Part B

Andreas Hauptmann received his PhD in 2017 from the University of Helsinki in Applied Mathematics. He currently holds a position as Academy Research Fellow and Associate Professor (tenure track) of Computational Mathematics at the Research Unit of Mathematical Sciences, University of Oulu, and as Honorary Associate Professor at the Department of Computer Science, University College London. His research interest is in inverse problems and tomographic imaging, with a focus on combining model-based inversion techniques with data-driven methods and the study of their theoretical properties. Bangti Jin received a PhD in Mathematics from The Chinese Universityof Hong Kong, Hong Kong in 2008. Previously, he was Lecturer and Reader, and Professor at Department of Computer Science, UniversityCollege London (2014-2022), an assistant professor of Mathematics at the University of California, Riverside (2013–2014), a visiting assistant professor at Texas A&M University (2010–2013), an Alexandre von Humboldt Postdoctoral Researcher at University of Bremen (2009–2010). Currently he is Professor of Mathematics, Global STEM Scholar, at The Chinese University of Hong Kong. His research interests include inverse problems, numerical analysis and machine learning. Carola-Bibiane Schönlieb graduated from the Institute for Mathematics, University of Salzburg (Austria) in 2004. From 2004 to 2005 she held a teaching position in Salzburg. She received her PhD degree from the University of Cambridge (UK) in 2009. After one year of postdoctoral activity at the University of Göttingen (Germany), she became a Lecturer at Cambridge in 2010, promoted to Reader in 2015 and promoted to Professor in 2018. Since 2011 she is a fellow of Jesus College Cambridge. She currently is Professor of Applied Mathematics (2006) at the University of Cambridge where she is head of the Cambridge Image Analysis group. Her current research interests focus on variational methods, partial differential equations and machine learning for image analysis, image processing and inverse imaging problems.

• 1. Deep Image Prior for Computed Tomography2. A Unified Framework for Lifted Training and Inversion Approaches3. Learned Regularization for Inverse Problems: Potentials and Challenges of Generative Models4. Learned Regularization for Inverse Problems: Potentials and Challenges of Generative Models5. Data Consistent Learning of Inverse Problems6. Yet to Be Decided7. Self-Supervised Deep Learning for Inverse Imaging Problems8. Data Selection: At the Interface of PDE-Based Inverse Problems and Randomized Linear Algebra9. Learning Regularization Functionals for Inverse Problems: A Comparative Study10. Coupling Deep Learning with Full Waveform Inversion11. Solving Imaging Inverse Problems Using Plug-and-Play Denoisers: Regularization and Optimization Perspectives12. Diffusion Models for Inverse Problems13. Neural Networks for Inverse Problems: From Representation to Learning Dynamics14. Numerical Analysis of Unsupervised Learning Approaches for Parameter Identification in PDEs15. On Generalised Hardness of Approximation, Hallucinations, Instability and Trustworthiness in AI for Inverse Problems16. Operator Learning Meets Inverse Problems17. Learned Iterative Networks: An Operator Learning Perspective