“I have a strong passion for developing open-source scientific software.” 

Software contributions

CLASSIX is a fast and explainable clustering algorithm based on sorting. CLASSIX is a contrived acronym of CLustering by Aggregation with Sorting-based Indexing and the letter X for explainability. CLASSIX clustering consists of two phases, namely a greedy aggregation phase of the sorted data into groups of nearby data points, followed by a merging phase of groups into clusters. The algorithm is controlled by two parameters, namely the distance parameter radius for the group aggregation and a minPts parameter controlling the minimal cluster size.


fABBA is a fast and accurate symbolic representation method for temporal data. It is based on a polygonal chain approximation of the time series followed by an aggregation of the polygonal pieces into groups. The aggregation process is sped up by sorting the polygonal pieces and exploiting early termination conditions. In contrast to the ABBA method [S. Elsworth and S. Güttel, Data Mining and Knowledge Discovery, 34:1175-1200, 2020], fABBA avoids repeated within-cluster-sum-of-squares computations which reduces its computational complexity significantly. Furthermore, fABBA is fully tolerance-driven and does not require the number of time series symbols to be specified by the user.



SNN is a fast exact fixed-radius nearest neighbor search algorithm. It uses the first principal component of the data to prune the search space and speeds up Euclidean distance computations using high-level BLAS routines. SNN is implemented in native Python. On many problems, SNN is faster than KDtree and Balltree in the scikit-learn package. There is also a C++ implementation of SNN.