Research Areas
- Applied & Computational Math
My research interests include convex analysis, matrix analysis, conic programming, and their applications to nonlinear optimization.
My current focus is on the relaxation of combinatorial optimization problems arising from applications in machine learning and high-dimensional statistics, specifically, convex relaxations for learning tasks such as clustering and classification.
I teach a variety of courses in the Department of Mathematics and conduct research in the area of optimization and its applications in engineering, statistics, and computer science.
Keywords: Optimization, machine learning, linear algebra, convex analysis.
Subject areas: Applied and Computational Math.
Current Projects: Optimization, machine learning.
