I was a researcher and I still currently am a PhD student in the field of data science. In particular, my PhD-related research is about feature selection with constraints. Leveraging constraints, one may select features not only based on their predictive quality but also consider aspects like domain knowledge or interpretability. Thus, constraints can make feature selection more user-centric. 

Additionally, I had several collaborations with other researchers from data science and application domains like materials science, process verification, and SAT solving. Many of my teaching and research activities correspond to a GitHub project that contains the code and sometimes further materials. Further, I published Python packages for four of my research projects on PyPI:

• alfese: Alternative feature selection - Find multiple feature sets (sequentially or simultaneously) that optimize feature-set quality while being sufficiently dissimilar to each other. Version 1.0.0 of the package supports five feature-selection methods.

• cffs: Constrained (filter) feature selection - Optimize a linear feature-set quality function (univariate filter approach) while considering user constraints formulated in propositional logic and linear arithmetic.

• csd: Constrained subgroup discovery - Subgroup discovery (1) without constraints, (2) with a limited number of features in the subgroup description, and (3) for finding alternative subgroup descriptions. Version 1.0.0 of the package supports seven subgroup-discovery methods.

• kpsearch: K-portfolio search - Given the runtimes of multiple algorithms on multiple problem instances, find a subset (with predefined size k) of algorithms which is overall fastest if all algorithms are run in parallel  on each instance (or, equivalently, if you have an oracle that always chooses the fastest solver per instance). Version 1.0.0 of the package currently supports seven portfolio-search methods.

My publication and the corresponding experimental data are listed in the following.

I taught the exercises of "Data Science 1" (old name: "Big Data Analytics") three times and the practical course "Data Science Laboratory Course" (old name: "Analyzing Big Data Laboratory Course") five times. I fundamentally re-designed the exercises of "Data Science 1" when acquiring the "Baden-Württemberg Certificate for Teaching and Learning at University Level". Further, I supervised one project each for the courses "Software Engineering in Practice" ("Praxis der Softwareentwicklung"; project topic: "CS:Select -  A Game for Feature Selection in Machine Learning") and "Research Project" ("Praxis der Forschung"; project topic: "Automating SAT Solver Research"). Finally, I supervised three seminar ~, seven bachelor's ~, and three master's theses.