I am a post-doctoral research fellow at Harvard School of Engineering and Applied Science, under the support of the Croucher Foundation Post-Doctoral Research Fellowship.

My current research focuses on statistical signal processing for large-scale data, with long term efforts in developing theories and algorithms to recover latent structures and processes leveraging the data. Topics of interest include signal reconstruction, sampling theory, network analysis and numerical optimization. In particular, I work on the following projects:

  • Imaging:
    • (1) Random Sampling for Image Filtering
    • (2) Single-Photon Imaging Sensors
  • Network Analysis:
    • (1) Nonparametric Graphon Estimation by Stochastic Block Approximation
    • (2) Total Variation based Graphon Estimation Algorithms

My advisors at Harvard SEAS are Yue M. Lu and Todd Zickler. I also work with Edo Airoldi at the Department of Statistics.

You are welcome to visit my old website at UC San Diego: http://videoprocessing.ucsd.edu/~stanleychan


"Image Denoising by Targeted External Databases" is accepted to ICASSP 2014

February 8, 2014

Classical image denoising algorithms based on single noisy images and generic image databases will soon reach their performance limits. In this paper, we propose to denoise images using targeted external image databases. Formulating denoising as an optimal filter design problem, we utilize the targeted databases to (1) determine the basis functions of the optimal filter by means of group sparsity; (2) determine the spectral coefficients of the optimal filter by means of localized priors. For a variety of scenarios such as text images, multiview images, and face images, we demonstrate superior denoising results over existing algorithms.

Enming Luo, Stanley H. Chan, and Truong Q. Nguyen, “Image Denoising by Targeted External Databases”, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2014, pp.3019-3023. (Awarded ICASSP Student Travel Grant)

"A Consistent Histogram Estimator for Exchangeable Graph Models" is accepted to ICML 2014

February 3, 2014

Exchangeable graph models (ExGM) subsume a number of popular network models. The mathematical object that characterizes an ExGM is  termed a graphon. Finding scalable estimators of graphons, provably consistent, remains an open issue. In this paper, we propose a histogram estimator of a graphon that is provably consistent and numerically efficient. The proposed estimator is based on a sorting-and-smoothing (SAS) algorithm, which first sorts the empirical degree of a graph, then smooths the sorted graph using total variation minimization. The consistency of the SAS algorithm is proved by leveraging  sparsity concepts from compressed sensing.

Accepted to International Conference on Machine Learning 2014 (1st round acceptance rate: 85/577 = 15.7%).

Paper is available at http://jmlr.org/proceedings/papers/v32/chan14.html

Reproducible MATLAB code is available at https://github.com/airoldilab/SAS

Monte Carlo Non-Local Means submitted to IEEE Trans. Image Process.

January 6, 2014

A new paper "Monte Carlo non local means: Random sampling for large-scale image filtering" is submitted to IEEE Trans. Image Process. (2014). In this paper, we propose a randomized version of the non-local means (NLM) algorithm for large-scale image filtering. The new algorithm, called Monte Carlo non-local means (MCNLM), speeds up the classical NLM by computing a small subset of image patch distances, which are randomly selected according to a designed sampling pattern. 

Paper is available a http://arxiv.org/abs/1312.7366

Reproducible MATLAB code will be available after the paper is published.