I am an Assistant Professor of Electrical Engineering at Harvard University, directing the Signals, Information, and Networks Group (SING) at the School of Engineering and Applied Sciences.

My research interests include signal processing using methods from statistical physics, spatiotemporal single-photon imaging, multidimensional sampling theory, multiscale geometrical representations, and image processing.

Research at SING is supported by NSF, Mass General Hospital, and Agilent Technologies. I am also grateful to the Croucher Foundation, the MIT Lincoln Lab Scholar Program, the NSF Graduate Research Fellowship Program, and the NCSA Blue Waters Graduate Fellowship Program for supporting current and past members of my group.


New Member of the Group

New Member of the Group

February 17, 2015

Dr. Chuang Wang just joined SING on Feb. 1 as a postdoctoral fellow. Chuang received his Ph.D. degree in theoretical physics from the Chinese Academy of Sciences, and he was a visiting student at SING in 2014. Chuang has worked on spin glasses, random optimization problems, and message passing algorithms. Welcome, Chuang!

Associate Editorship for IEEE TIP

December 17, 2014

I have been appointed as an associate editor for the IEEE Transactions on Image Processing, with a three-year term through December 1, 2017.

IEEE IVMSP Technical Committee

November 14, 2014

I have been elected to serve on the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee for a 3-year term starting January 2015.

Randomized Kaczmarz algorithm: Annealed and quenched error exponents

October 22, 2014

The Kaczmarz method is a popular method for solving large-scale overdetermined systems of equations. Recently, Strohmer et al. proposed the randomized Kaczmarz algorithm, an improvement that guarantees exponential convergence to the solution. This has spurred much interest in the algorithm and its extensions. In our paper, we provide an exact formula for the mean squared error (MSE) in the value reconstructed by the algorithm.

Fast image reconstruction for spatiotemporal single photon imaging

October 1, 2014

Recent advances in materials, devices and fabrication technologies have spurred strong research interests in developing solid-state sensors that can detect individual photons in space and time. In our paper, we present an efficient algorithm to reconstruct images from the massive bit-streams generated by these sensors. 

Group member to join Purdue University

Group member to join Purdue University

May 22, 2014

Stanley Chan, a postdoc at SING, will join Purdue University this August as a tenure-track Assistant Professor of ECE and Statistics. Congratulations and best wishes, Stanley!

Randomized algorithms for large-scale image filtering

January 8, 2014

We propose a randomized version of the non-local means (NLM) algorithm for large-scale image filtering. When applied to denoising images using an external database containing ten billion patches, our algorithm returns a randomized solution that is within 0.2 dB of the full NLM solution while reducing the runtime by three orders of magnitude. See our paper for more details.

New paper: Sparse representation à la Prony

October 23, 2013

We consider the classical problem of finding the sparse representation of a signal in a pair of bases. When both bases are orthogonal, it is well-known that the sparse representation is unique when the sparsity $K$ of the signal satisfies $K<1/\mu(\mD)$, where $\mu(\mD)$ is the mutual coherence of the dictionary. Furthermore, the sparse representation can be obtained in polynomial time by Basis Pursuit (BP), when $K<0.91/\mu(\mD)$. Therefore, there is a gap between the unicity condition and the one required to use the polynomial-complexity BP formulation.