# ECE Seminar with Sidharth Jaggi

- Starts:
- 4:00 pm on Wednesday, March 6, 2013
- Location:
- Photonics Center, 8 Saint Mary’s St., Room 339
- URL:
- http://www.bu.edu/ece/files/2013/03/Jaggi.pdf

Robust Sparse Recovery and Applications: Order-Optimal Measurements and Complexity

With Sidharth Jaggi

Department of Information Engineering

Chinese University of Hong Kong

Faculty Host: Bobak Nazer

Refreshments will be served outside Room 339 at 3:45 p.m.

Abstract: Sparse recovery problems are usually in the setting in which a vector x with ambient dimension n has only k "significant" entries. This vector x is "compressively measured" (with possibly "noisy" measurements) as y, where y has just m<

We consider three sparse recovery problems:

* Compressive Sensing: A linear problem in which y is a (possibly noisy) linear transform of x, a vector in R^n.

* Group Testing: A non-linear problem in which y is a binary vector comprising of (possibly noisy) disjunctive measurements of a binary x vector.

* Network Tomography: A linear problem in which x, a vector in R^n, denotes network parameters (such as edge/node delays) to be estimated via constrained, end-to-end measurements (such as path delays).

In each of these cases we present sparse recovery algorithms that have good reconstruction performance and have information-theoretically order-optimal decoding complexity and number of measurements (O(k)measurements and decoding complexity for compressive sensing and network tomography and O(k log(n)) measurements and decoding complexity for group testing.)

About the Speaker: Sidharth Jaggi received his Bachelor of Technology degree from the Indian Institute of Technology in 2000 and his Master of Science and Ph.D. degrees from the California Institute of Technology in 2001 and 2006, respectively, all in electrical engineering. He was awarded the Caltech Division of Engineering Fellowship 2001-02, and the Microsoft Research Fellowship for the years 2002-04. He interned at Microsoft Research, (Redmond, WA) in the summers of 2002-03 and engaged in research on network coding. He spent 2006 as a postdoctoral associate at the Laboratory of Information and Decision Systems at the Massachusetts Institute of Technology. He joined the Department of Information Engineering at the Chinese University of Hong Kong in 2007.

With Sidharth Jaggi

Department of Information Engineering

Chinese University of Hong Kong

Faculty Host: Bobak Nazer

Refreshments will be served outside Room 339 at 3:45 p.m.

Abstract: Sparse recovery problems are usually in the setting in which a vector x with ambient dimension n has only k "significant" entries. This vector x is "compressively measured" (with possibly "noisy" measurements) as y, where y has just m<

We consider three sparse recovery problems:

* Compressive Sensing: A linear problem in which y is a (possibly noisy) linear transform of x, a vector in R^n.

* Group Testing: A non-linear problem in which y is a binary vector comprising of (possibly noisy) disjunctive measurements of a binary x vector.

* Network Tomography: A linear problem in which x, a vector in R^n, denotes network parameters (such as edge/node delays) to be estimated via constrained, end-to-end measurements (such as path delays).

In each of these cases we present sparse recovery algorithms that have good reconstruction performance and have information-theoretically order-optimal decoding complexity and number of measurements (O(k)measurements and decoding complexity for compressive sensing and network tomography and O(k log(n)) measurements and decoding complexity for group testing.)

About the Speaker: Sidharth Jaggi received his Bachelor of Technology degree from the Indian Institute of Technology in 2000 and his Master of Science and Ph.D. degrees from the California Institute of Technology in 2001 and 2006, respectively, all in electrical engineering. He was awarded the Caltech Division of Engineering Fellowship 2001-02, and the Microsoft Research Fellowship for the years 2002-04. He interned at Microsoft Research, (Redmond, WA) in the summers of 2002-03 and engaged in research on network coding. He spent 2006 as a postdoctoral associate at the Laboratory of Information and Decision Systems at the Massachusetts Institute of Technology. He joined the Department of Information Engineering at the Chinese University of Hong Kong in 2007.