I work on tools to visualize ML-Rules simulation results. The aim is to enable the user to evaluate and interpret results in a simple and intuitive manner.
I work on parameter optimization by implementing and testing different optimization algorithms.
The goal is to achieve a time-optimized simulation for different hardware by providing the respective optimal parameter values.
My job it to visualize data given by a variety of particle simulations.
I use OpenGL libraries to draw particles as spheroids in a three-dimensional space and provide a controllable camera to move through the scenes. In the future it'll be possible to record each visualization and save them as a file. Another planned feature is the tracking of individual particles to simplify following their paths and observing their behavior.
I am working on implementing methods for sensitivity analysis.
Sensitivity analysis deals with the question of how a model reacts to input parameter changes. The goal is to gain a better understanding of the models behavior to facilitate decision-making.
I improve the runtime efficiency of ML-Rules simulations by generating Java code at runtime from abstract syntax trees. Parts of these syntax trees are then replaced by the corresponding generated code.
I am developing a tool for interactive experiment design that enables exploring the parameter space of complex models.
The method relies on model-specific visualizations tailored to concrete applications.
I am working in a joint project with the scientific computation group of Prof. Starke from the institute of mathematics. My job is to implement several programs for analyzing the stability of complex systems.