The KeOps library (http://www.kernel-operations.io) provides routines to compute generic reductions of large 2d arrays whose entries are given by a mathematical formula. Using a C++/CUDA-based implementation with GPU support, it combines a tiled reduction scheme with an automatic differentiation engine. Relying on online map-reduce schemes, it is perfectly suited to the scalable computation of kernel dot products and the associated gradients, even when the full kernel matrix does not fit into the GPU memory.
Python package for haplotype phasing and local ancestry inference
Probabilistic count matrix factorization for single cell transcriptomic data analyses (dimension reduction, visualization).
Optimization toolbox for sparse estimation, implementing algorithms that solve machine learning and signal processing problems involving sparse regularizations.
Supervised methods for dimension reduction in classification and regression framework (in particular PLS-based routines for genomic data analyses).