Rafael Ballester-Ripoll


We contribute the first algorithm that can extract the full set of Sobol indices from a tensor surrogate model, as well as many other advanced sensitivity metrics. This allows us, among others, to measure interactions of arbitrary order between input variables of complex systems.

Scientific Visualization

Data Compression

Large Scale Signal Processing & Rendering

We propose a 3D/4D compression algorithm that exploits the Tucker decomposition, together with bit-plane and arithmetic coding, to achieve extreme compression rates in real-world data sets such as tomographic scans and turbulent physical simulations.

We develop GPU-accelerated algorithms for lar-ge tensors that enable efficient level-of-detail rendering and signal processing operations  of gigabyte-scale volume data sets: 3D convolution, denoising, computing histograms, progressive reconstruction, etc.


I am a Postdoctoral Associate jointly affiliated with University of Zurich's VMML Lab (Prof. Renato Pajarola, who was my PhD advisor) and with ETH Zurich's CSE Lab (Prof. Petros Koumoutsakos).


My research interests include scientific visualization, sensitivity analysis, tensor decompositions, and surrogate modeling.


Sensitivity Analysis


We model and visualize multidimensional para-meter spaces (physical simulations, dynamical systems, reaction networks) using the tensor train decomposition. Users can interactively reduce the model's dimensionality, explore it, and extract informative statistics.


tntorch is a PyTorch-powered library for tensor network modeling and learning. It supports tensor decomposition and mani-pulation, statistics and sensitivity metrics, classification, optimization (using auto-differentiation), and more. It includes numerous tutorials covering all featured use cases.