Computational and Applied Mathematics
6100 Main Street
CAAM Dept. MS-134
Houston, TX 77005
- Applied Math
- Compressed Sensing
- Machine Learning
- Machine Vision
- Phase Retrieval
- Signal Recovery
I'm an applied mathematician interested in signal recovery problems. My current research focus is to develop new and faster ways of recovering signals in a variety of noisy contexts. I'm particularly interested in finding and simplifying convex programs that have provable recovery guarantees.
My Ph.D. research was in the derivation and simulation of macroscale partial differential equations that govern the electrical behavior of cardiac muscle cells.
I am also interested in math instruction with a focus on how students learn the mathematical problem solving process. My recent hobby has been to write the website Leading Lesson that makes the problem solving process explicit for over a hundred multivariable calculus problems.
- ShapeFit: Exact location recovery from corrupted pairwise directions (with Choongbum Lee and Vladislav Voroninski), 2015. (pdf) (code)
- Exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences (with Choongbum Lee and Vladislav Voroninski), 2015. (pdf)
- PhaseLift is robust to a constant fraction of arbitrary errors, 2015. (pdf)
- Stable optimizationless recovery from phaseless linear measurements (with Laurent Demanet). J. Fourier Anal. Appl., 20(1):199-221, 2014. (pdf) (code)
- Deriving Macroscopic Myocardial Conductivities by Homogenization of Microscopic Models (with Boyce Griffith and Charles Peskin). Bulletin of Mathematical Biology. 71: 1707-1726, 2009. (pdf)
Current Grant Support
My work is partially supported by National Science Foundation grant DMS-1464525.
Here is a talk I gave to high school students about the intersection of signal recovery theory, information theory, and compression:
Here is a website I developed that contains over 100 worked examples in multivariable calculus: