Dr. Justin Romberg is the Schlumberger Professor and the Associate Chair for Research in the School of Electrical and Computer Engineering and the Associate Director for the Center for Machine Learning at Georgia Tech.
Dr. Romberg received the B.S.E.E. (1997), M.S. (1999) and Ph.D. (2004) degrees from Rice University in Houston, Texas. From Fall 2003 until Fall 2006, he was a Postdoctoral Scholar in Applied and Computational Mathematics at the California Institute of Technology. He spent the Summer of 2000 as a researcher at Xerox PARC, the Fall of 2003 as a visitor at the Laboratoire Jacques-Louis Lions in Paris, and the Fall of 2004 as a Fellow at UCLA's Institute for Pure and Applied Mathematics. In the Fall of 2006, he joined the Georgia Tech ECE faculty. In 2008 he received an ONR Young Investigator Award, in 2009 he received a PECASE award and a Packard Fellowship, and in 2010 he was named a Rice University Outstanding Young Engineering Alumnus. He is currently on the editorial board for the SIAM Journal on the Mathematics of Data Science, and is a Fellow of the IEEE.
His research interests lie on the intersection of signal processing, machine learning, optimization, and applied probability.
- Imaging inverse problems
- Data compression
- Multiscale geometrical representations
- Sparse approximation
- ONR Young Investigator Award, 2008
- Presidential Early Career Award in Science and Engineering (PECASE), 2009
- Packard Fellowship, 2009
- Rice University Outstanding Young Engineering Alumnus, 2010
M. Wakin, J. Romberg, H. Choi, and R. Baraniuk, "Wavelet-domain approximation and compression of piecewise smooth images," IEEE Transactions on Image Processing, Vol. 15, Number 5, May2006, pp 1071-1087.
E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, Vol. 52, Number 2, February 2006, pp 489-509.
E. Candes, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Communications on Pure and Applied Mathematics, Vol. 59 (2006), Number 8, 1208-1223.
E. Candes and J. Romberg, "Quantitative robust uncertainty principles and optimally sparse decompositions," Foundations of Computational Mathematics, Vol. 6 (2006), Number 2, pp 227-254.
Last revised August 31, 2020