Research interests

Besides mathematics and computer science in general currently I am working on the following topics:

• analysis and improvement of open data related to public transport
• development of self-assessment solutions for Python programming in the Jupyter ecosystem
• applications of machine learning in medical imaging, control of energy supply networks and other fields
• theoretical foundations of ill-posedness in inverse problems theory

Other topics I’ve worked on in my academic life include:

• regularization methods for Laplace DLTS (deep-level transient spectroscopy)
• modern concepts in convergence rate theory for regularization methods with focus on Tikhonov-type regularization
• application of modern convergence rate theory to settings not manageable with classical techniques
• analysis of structured nonlinear mappings in connection with ill-posedness and regularization; in particular auto-convolution problems and other mappings with quadratic structure
• machine learning techniques for choosing regularization parameters in inverse problems
• connections between theory of machine learning and convergence rate theory for regularization of inverse problems
• relations between artificial neural networks and classical (that is, non-machine-learning) algorithms