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.

I do research in the field of sensing, representation, and processing of high-dimensional signals, as well as applications in high throughput spatiotemporal single-photon imaging. Keywords: statistical signal processing (using approaches from statistical physics), sampling theory, multiscale geometrical representations, imaging and image processing.

Research at SING is supported by NSF, Mass General Hospital, the MIT Lincoln Scholar Program, the Croucher Foundation, and Agilent Technologies.

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!

Group member receiving Blue Waters Fellowship

April 10, 2014

Ariana Minot, a Ph.D. student at SING, received the prestigious Blue Waters Graduate Fellowship. Congratulations, Ariana!

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.

Paper in Foundation and Trends in Signal Processing

September 30, 2012

The long overview paper, Multidimensional Filter Banks and Multiscale Geometric Representations, provides a systematic development of the theory and constructions of multidimensional filter banks and sparse representations that can efficiently capture directional and geometric features of multidimensional signals.

Welcome to new group member

August 30, 2012
Welcome to Ariana Minot, who joined my group in August. Ariana received her Bachelor of Science degree in Physics and Mathematics from Duke University in 2010. She is a Ph.D. student in Harvard's applied math program, and a recipient of a three-year NSF Graduate Research Fellowship.