I am a post-doctoral research fellow at Harvard School of Engineering and Applied Science, under the support of Croucher Foundation post-doctoral research fellowship (2012-2013).
My current research focuses on statistical signal processing for large data. I am interested in designing and analyzing tools for signal reconstruction, inference and detection. In particular, I work on:
- randomized algorithms for massive matrix computation;
- large deviation principles for statistical physics;
- estimating the graph limit - graphon.
You are welcome to visit my old website at UC San Diego: http://videoprocessing.ucsd.edu/~stanleychan
Our paper "Fast non-local filtering by random sampling" is also accepted for poster presentation in IEEE International Conference on Computational Photography (ICCP 2013).
A new paper "Fast non-local filtering by random sampling" is accepted to IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2013). In this paper, Yue and I applied large deviation principles (LDP) to study a very popular class of image denoising algorithms known as the non-local filtering. We showed that with extremely high probability, the computationally intensive non-local filtering operations can be approximated using a Monte Carlo random sampling scheme. In addition, we found that with one additional cost-less step by normalizing the columns of the graph adjacency matrix, the asymmetric non-local filter becomes symmetrized according to Sinkhorn-Knopp. This symmetrized filter further boosts up denoising quality by a big margin.