My research interests broadly lie in mathematical
optimization and its application to the information
sciences. I seek a deeper understanding of the power
as well as the limitations of convex optimization, with a
focus on the development
of efficient algorithms for challenging problems in
statistical signal processing. A
recurring theme in my work is the prominent role played by
various notions of algebraic structure in explaining the
effectiveness of convex relaxation methods.
- J. Saunderson, V. Chandrasekaran, P. A. Parrilo, and
A. S. Willsky, Diagonal and
Low-Rank Matrix Decompositions, Correlation Matrices, and
Ellipsoid Fitting, SIAM Journal on Matrix Analysis
and Applications, Vol. 33, No. 4, December 2012.
- V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky,
Convex Graph Invariants, SIAM Review, Vol. 54,
No. 3, September 2012.
- received the 2012 Jin-Au Kong Outstanding
Dissertation Award in Electrical Engineering from MIT's
Department of Electrical Engineering and Computer Science.
Selected conference papers
Last updated: November 2015