# Group member to join Purdue University

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!

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, statistical mechanics, 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.

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!

April 10, 2014

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

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.

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. Read more about New paper: Sparse representation à la Prony

June 17, 2013

Imagine that you are blindfolded inside an unknown room. You snap your fingers and listen to the room’s response. Can you hear the shape of the room? Some people can do it naturally, but can we design computer algorithms that hear rooms? Read more about Can you hear the shape of a room? Paper appears at PNAS

March 9, 2013

Recent advances in materials, devices and fabrication technologies have led to an emerging class of solid-state sensors that can detect individual photons in space and time. In our paper, Adaptive sensing and inference for single-photon imaging, we present models, theory and algorithms of adaptive sensing, allowing one to expand the dynamic ranges of these single-photon sensors.

March 1, 2013

Two papers from SING will be presented at ICASSP 2013:

- A. Agaskar and Y. M. Lu, Detecting random walks hidden in noise: Phase transition on large graphs, ICASSP 2013.
- S. Chan, T. Zickler and Y. M. Lu, Fast non-local filtering by random sampling: It works, especially for large images, ICASSP 2013.

February 16, 2013

Our paper, Matched signal detection on graphs: Theory and application to brain network classification, has been accepted at the 23rd International Conference on Information Processing in Medical Imaging (IPMI 2013), a highly selective forum in the field of medical imaging. In the paper, we develop a matched signal detection theory for signals with an intrinsic structure described by a weighted graph.

January 15, 2013

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.

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.

May 23, 2012

The spectral theory of graphs provides a bridge between classical signal processing and the nascent field of graph signal processing. In the paper A Spectral Graph Uncertainty Principle, we investigate a spectral graph analogy to Heisenberg's celebrated uncertainty principle. Read more about A Spectral Graph Uncertainty Principle

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