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For me, physics always meant having a rich toolbox of mathematical and scientific methods to choose from. That all these tools are usually mostly found in the application to physical problems does not mean that they should not be used outside of it, quite to the contrary.
When I was doing my final thesis on the quantitative simulation of quantum cascade lasers I noticed that a new field was gaining importance and momentum: Computational Intelligence (CI) and Machine Learning (ML). That basically meant for me the implementation of physical methods in computer science.
I was happy to find a position as doctoral student with Prof. Dr. E. Lang who is an expert in this field and later with Prof. Dr. C. G. Puntonet who showed my the importance of visualisation and geometric interpretations.
Computational Intelligence and Machine Learning are now the driving force behind almost all new developments in quantitative data analysis in the recent years. But only by understanding the fundamental mathematical basics it is possible to develop and apply new methods in the field to real world applications. This is why I specialised in blind source separation (NMF, NTF, ICA, PCA), while keeping an eye on optimisation (Neural Networks, Bio-inspired Optimisation), classification (Random Forrest, SVM, Neural Networks), clustering (Self Organising Maps, Neural Gas), and time series evaluation (Empirical Mode Decomposition, regression).
If you want to find out more about my research have a look at my software projects and my publications!