Mathematical models must be tailored to the question at hand. All available information including data as well as system understanding should be considered. As a statistician my interests lay in constructing adequate models by means of statistics, machine learning and applied mathematics to investigate scientific hypotheses or support decision makers.
Besides the mathematical/technical aspects of modeling I’m also very interested in communication. Conveying to the users the underlying model assumptions, the interpretation and limits of the results is fundamental for a successful application of any model.
Methods and Tools
Some topics and methods I work with or I am interested in:
- Bayesian Inference
- Gaussian Processes
- Data assimilation(Deep) Artificial Neuronal Networks
- Uncertainty Quantification
- Causal Inference
- Graphical (hierarchical) Models
For implementation I use among others Julia, R, Python, STAN, Emacs.