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 am working with algorithms for pattern matching in rule-based modeling languages.
I am assessing existing algorithms in terms of efficiency and correctness. Subsequently, I adapt those algorithms or develop new ones.
By electrically stimulating neural stem cells I am studying the effects of electrical fields on these cells. For this, I am applying immunofluorescence to mark proteins of interest. Our goal is to use the obtained data for developing a cellular model that may explain some of the behavior of the cells.
I analyze scientific publications to extract information about simulation studies and in particular about simulation experiments.
By using various methods from natural language processing and machine learning, I develop a pipeline that extracts semantic information from unstructured texts.