My PhD project focuses on interactive visualization and exploration of complex and/or large-scale multidimensional data sets, e.g. micro-computed/synchrotron tomography, parameter spaces, surrogate models, etc. A fundamental tool in my research is compression and feature extraction by means of tensor approximation. The goal is to develop algorithms for compression, feature extraction and processing in the tensor compressed format such as the Tucker decomposition or the tensor train (TT). Related techniques include 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)