The project we will be working on is achieving super-resolution in inverse problems. It consists of a class of problems arising from seismic and medical imaging where sharp reconstructions/images are desired from band-limited observations. We utilize applied and computational harmonic analysis with an emphasis on compressed sensing and convex optimization theory. Prior experience in inverse problems would be a plus. The successful candidate will be working with Dr. Rongrong Wang. Preferred starting date no later than Sep 1, 2018.
Ph.D in Applied Mathematics, University of Maryland College Park, 2013
Advisor(s): John Benedetto, Wojciech Czaja.
B.S. in Mathematics, Peking University, 2007
B.A. in Economics, Peking University, 2007
Rongrong Wang received her B.S. in Mathematics and her B.A. in Economics from Peking University, Beijing, China, in 2007. In 2013, Rongrong recieved her Ph.D in Applied Mathematics from the University of Maryland College Park.
While at the University of Maryland College Park Rongrong recieved an Academic Excellence Award and the Ruth Davis Award for oustanding acadmeic accomplishments. From 2013 to 2017, Rongrong was a postdoc at the University of British Columbia's Department of Mathematics and Department of Earth, Ocean, and Atmospheric Sciences. During her postdoc Rongrong was a researcher of Seismic image inversion, supported by Seismic Imaging by Next-Generation Basis Functions Decomposition (SINBAD).
As of 2017, Rongrong has joined the Department of Computational Mathematics, Science and Engineering at Michigan State University.
For more information: http://math.msu.edu/~wangrong/
• Compressed Sensing
• Sigma Delta Quantization
• Frame Theory
• Convex Optimization, Sparse Signal Recovery
• Inverse Problems in Geophysics.
– Variants of Full Waveform Inversion
– Blind Deconvolution
FS-17: MTH 132 Caluclus I
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