## Expertise

Matrix completion, compressive sensing, distance geometry

## Research Interests

My research is broadly in applied mathematics with current research interests at the intersection of matrix completion, compressive sensing, high-dimensional probability and convex optimization. In the theoretical side, my work has used tools from probability to assess the effectiveness of certain convex optimization algorithms. The problems I study originate from consideration of real world applications. As such, they motivate the construction of a mathematical model and the design of algorithms that could handle noisy/incomplete data. A common thread in my research is a theoretical analysis of convex approximations and a principled design of algorithms for possibly large scale problems.

Some of my research topics are:

- Algorithms for the distance geometry problem with the goal of determining 3D protein structure
- Fast convex algorithms for the graph matching problem
- Theoretical analysis of deep learning in certain applications.