Romberg

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[[Image:Romberg.jpg|thumb|left|442px|Justin Romberg]]
[[Image:Romberg.jpg|thumb|left|442px|Justin Romberg]]
Justin Romberg received the BS (1997), MS (1999), and PhD (2003) degrees in Electrical and Computer Engineering from Rice University.
Justin Romberg received the BS (1997), MS (1999), and PhD (2003) degrees in Electrical and Computer Engineering from Rice University.
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 Romberg co-authored many publications with Professor Richard Baraniuk at Rice, and was a Texas Instruments Distinguished Graduate Fellow.
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 Romberg spent Fall 2003 as a visitor at Laboratoire Jacques-Louis Lions at Paris VI,
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and Fall 2004 as a visiting Fellow at UCLA's Institute for Pure and Applied Mathematics.
 He was a Postdoctoral Scholar in Applied and Computational Mathematics at Caltech from 2003-2006.
 He was a Postdoctoral Scholar in Applied and Computational Mathematics at Caltech from 2003-2006.
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 At Rice, Romberg co-authored many publications with Professor Richard Baraniuk, and was a Texas Instruments Distinguished Graduate Fellow.
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 He joined Georgia Institute of Technology in 2006 as Assistant Professor in the School of Electrical and Computer Engineering.
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 He joined Georgia Institute of Technology in 2006 as an Assistant Professor in the School of Electrical and Computer Engineering.
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Justin Romberg spent Fall 2003 as a visitor at Laboratoire Jacques-Louis Lions at Paris VI,
 
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and Fall 2004 as a visiting Fellow at UCLA's Institute for Pure and Applied Mathematics.
 
Justin Romberg won the Office of Naval Research Young Investigator Award in 2008
Justin Romberg won the Office of Naval Research Young Investigator Award in 2008
for his proposal “Compressive Sampling for Next-Generation Signal Acquisition".
for his proposal “Compressive Sampling for Next-Generation Signal Acquisition".
 Dr. Romberg's research centers on mathematics of data acquisition.
 Dr. Romberg's research centers on mathematics of data acquisition.
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 In particular, he is interested in how randomness can actually help in data acquisition, potentially reducing both cost and computational complexity of high-resolution sensing systems.
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 He is interested in how randomness increases efficiency in data acquisition, in particular, reducing both cost and computational complexity of high-resolution sensing systems.
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 This work will influence the design of next-generation analog-to-digital converters, radar imaging platforms, and MRI systems.
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 This work will influence the design of next-generation analog-to-digital converters, radar imaging platforms, and Magnetic Resonance Imaging (MRI) systems.
== Compressed Sensing: A Tutorial ==
== Compressed Sensing: A Tutorial ==
[http://users.ece.gatech.edu/~justin/ssp2007/ssp07-cs-tutorial.pdf by Justin Romberg & Michael Wakin presented at IEEE 14th Workshop on Statistical Signal Processing, Madison Wisconsin, August 26, 2007]
[http://users.ece.gatech.edu/~justin/ssp2007/ssp07-cs-tutorial.pdf by Justin Romberg & Michael Wakin presented at IEEE 14th Workshop on Statistical Signal Processing, Madison Wisconsin, August 26, 2007]

Revision as of 15:11, 23 August 2008

JUSTIN ROMBERG

Justin Romberg
Justin Romberg

Justin Romberg received the BS (1997), MS (1999), and PhD (2003) degrees in Electrical and Computer Engineering from Rice University.  Romberg co-authored many publications with Professor Richard Baraniuk at Rice, and was a Texas Instruments Distinguished Graduate Fellow.  Romberg spent Fall 2003 as a visitor at Laboratoire Jacques-Louis Lions at Paris VI, and Fall 2004 as a visiting Fellow at UCLA's Institute for Pure and Applied Mathematics.  He was a Postdoctoral Scholar in Applied and Computational Mathematics at Caltech from 2003-2006.  He joined Georgia Institute of Technology in 2006 as Assistant Professor in the School of Electrical and Computer Engineering.


Justin Romberg won the Office of Naval Research Young Investigator Award in 2008 for his proposal “Compressive Sampling for Next-Generation Signal Acquisition".  Dr. Romberg's research centers on mathematics of data acquisition.  He is interested in how randomness increases efficiency in data acquisition, in particular, reducing both cost and computational complexity of high-resolution sensing systems.  This work will influence the design of next-generation analog-to-digital converters, radar imaging platforms, and Magnetic Resonance Imaging (MRI) systems.

Compressed Sensing: A Tutorial

by Justin Romberg & Michael Wakin presented at IEEE 14th Workshop on Statistical Signal Processing, Madison Wisconsin, August 26, 2007

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