- Computational linear algebra, especially fast and robust algorithms
for large-scale eigen-related problems, with applications in materials science,
dimensionality reduction, and numerical optimization.
- Currently also studying theory and fast algorithms related to large-scale
statistical computing, data mining and machine learning,
including dimensionality reduction for visualization/interpretation of
high-dimensional data, especially techniques that can effectively reveal the
(such as possible low-dimensionality, low-rank, high sparsity, or non-negativity)
of the original underlying problems.
- Software development.
Links of interest:
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