Ph.D. Student: Industrial and Operations Engineering
Victor Fuentes is interested in the study and refinement/improvement of algorithmic theory and practice in non-convex optimization. His current research is in the area of global and sparse nonlinear optimization, with a focus on matrix decomposition problems, as seen in fields such as compressed sensing. In particular, his work focuses on the construction of optimization models that utilize convexity relaxations techniques and approximation/tightening methods to evaluate nonlinear/non-convex optimization problems.
Advisor: Jon Lee
Position Sought: Research, Industry, Academia