This section contains some selected open source projects. You can find most of my projects on my GitHub.
Co-author of the widely used machine learning library for R that includes over 80 classification methods and over 50 regression methods. mlr also offers hyperparameter optimization, as well as pre- and post-processing methods. I implemented many learners, Bayesian optimization for tuning, fixed various bugs, took care of unit tests, documentation and code-reviews.
Co-author of the successor of mlr. I contributed to the early development and new object-oriented design of mlr3. The mlr3verse consist of various R packages that are developed in a big team across universities. I largely commit on doing code-reviews and helping others to contribute to the project. My main contributions are to the packages related to tuning mlr3tuning, bbotk and mlr3mbo.
With this small side-project I simplified hyperparameter tuning within mlr by defining a heuristic that choses the appropriate search space and optimizer for a given machine learning algorithm and dataset. More details in my blog post on r-bloggers and my talk at the international useR! conference 2017.
Maintainer and co-author of the model-based optimization toolkit for R: mlrMBO. This R package offers various Bayesian optimization methods and includes parallelization as well as multi-criteria optimization. Most of my research is implemented in this package and partly resides in development-branches. You can see my presentation of mlrMBO at the useR! conference 2016 in Stanford here.