Velkommen!

I am fascinated by the mathematical handling of data - collecting it, processing it, training networks on it and recovering physical parameters from it. I'm equally happy to run the mathematical analysis as I am writing sleek and efficient code.

My main interests include

• Neural networks and machine learning represents the most modern approach to data processing and modelling, and is a vibrant field with huge advances constantly appearing. Being on the cutting edge means not only learning how to implement, train and utilise hyper-efficient neural network architectures, but also to deepen our understanding of how machine learning works and how to interpret the results.

• Finite element methods are fast, reliable, well-understood and provide both excellent ground truth-type simulations, as well as allowing detailed mathematical analysis. A cornerstone of physical analysis, finite elements both feature heavily in my physics-based models, and I enjoy exploiting their mathematical properties to find computational shortcuts.

• Optimal experimental design is the mathematical study of devising experiments and collecting experimental data in a manner that leads to the best possible reconstruction results. This is exciting field involves inverse problems, stochastics, partial differential equations, large-scale computing, matrix algebra and more.