Rafael Ballester, Dr.

Rafael Ballester, Dr.

Postdoc/Oberassistent - BIN 2.C.05

Phone: +41 44 635 75 38



My research focuses on analysis and visualization of complex, multidimensional and/or large-scale data sets and models ranging from micro-computed/synchrotron tomography to simulation parameter spaces and surrogate models. The mathematical framework I use is tensor decomposition (mostly the tensor train (TT), the Tucker model, and other tensor networks). Tensor methods lend themselves very well to data manipulation in the compressed domain, including derivation/integration, convolution, element-wise operations, statistical moments, etc. I used these tools in my PhD for multidimensional compression, filtering, surrogate modeling, sensitivity analysis, and feature extraction, among others.



  • tntorch: Tensor Network Learning with PyTorch (Python)
  • ttrecipes: a cookbook of algorithms and tools using the tensor train format (Python)
  • TTHRESH: a Tucker decomposition-based multidimensional compressor (C++)
  • vmmlib tensor library (C++)