SIAM News reports on our work on deep learning in scientific computing
The recent March edition of SIAM News reports on our work on deep learning in scientific computing.
You have reached the webpage of Vegard Antun, a postdoctoral fellow in mathematics at the University of Oslo. Here you can find information about my research interests, publications, an overview of my code and contact details.
Broadly speaking, I'm interested in applied mathematics, with a particular focus on inverse problems and imaging. In particular, I am interested in methods for recovery of signals (vectors, images, or functions) from a limited set of measurements. The study of such methods uses tools from many areas of mathematics including compressive sensing, approximation theory, mathematical signal processing as well as machine learning techniques such as deep learning. During the last few years, methods based on learning have started to outperform many standard methods, not only in computational imaging, but in scientific computing in general. Yet, many of these new approaches seem to have a common Achilles heel: stability. Much of my research focuses on the stability and accuracy of methods for inverse problems.
The recent March edition of SIAM News reports on our work on deep learning in scientific computing.
Our paper “On instabilities of deep learning in image reconstruction and the potential costs of AI” has been published
My PhD thesis has been approved for dissertation