# Associate Editorship for IEEE TIP

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

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 and the NCSA Blue Waters Graduate Fellowship Program for supporting current and past members of my group.

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.

December 6, 2014

Our paper, "Randomized Kaczmarz algorithms: Exact MSE analysis and optimal sampling probabilities," won the Best Student Paper Award at IEEE Global Conference on Signal and Information Processing (GlobalSIP). Congratulations to Ameya and Chuang!

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.

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. Read more about Randomized Kaczmarz algorithm: Annealed and quenched error exponents

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.

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

January 2, 2012

A. Agaskar and Y. M. Lu, Uncertainty principles for signals defined on graphs: Bounds and characterizations.

Y. Xiong and Y. M. Lu, Blind estimation and low-rate sampling of sparse MIMO systems with common support.

June 5, 2011

Before the advent of digital image sensors, photography, for the most part of its history, used film to record light information. In the paper Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics, we study a new digital image sensor that is reminiscent of photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. Read more about Imaging by One-Bit Pixels

March 21, 2011

The paper Can One Hear the Shape of a Room: The 2-D Polygonal Case was awarded the Best Student Paper Award at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in 2011. In a famous work, M. Kac asks the catchy question “Can you hear the shape of a drum?”. This problem is related to a question in astrophysics, and the answer is negative, meaning, different drum shapes can have the same resonant frequencies. Read more about ICASSP Best Student Paper Award

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