My PhD project focuses on interactive visualization and exploration of large-scale multidimensional data sets (e.g. micro-computed or synchrotron tomography). A fundamental tool in my research is compression and feature extraction by means of tensor approximation (TA). The goal is namely to develop algorithms for large data manipulation and analysis in the TA compressed format, such as the canonical format, the Tucker decomposition or the Tensor Train. Related techniques are 3D/4D multiresolution representations, progressive tensor rank reconstruction and out-of-core memory management.
More details on the data visualization and exploration projects:
- A Surrogate Visualization Model Using the Tensor Train Format (2016)
- Compressing Bidirectional Texture Functions via Tensor Train Decomposition (2016)
- Tensor Decomposition Methods in Visual Computing (tutorial) (2016)
- Structural Volume Inpainting via Tucker Dictionary Learning (poster) (2016)
- Analysis of Tensor Approximation for Compression-Domain Volume Visualization (2015)
- Lossy Volume Compression Using Tucker Truncation and Thresholding (2015)