Radoslav Vuchkov

Biography

Radoslav’s research interests are in optimization and numerical linear algebra. In particular large-scale adjoint-based inversion problems, uncertainty quantification, quasi-Newton methods, Krylov methods, and reduced order models for solving numerical methods for ordinary and partial differential equations.

Currently, he is focusing on inexact parallel-in-time solvers and SQP methods for PDE-constrained optimization and randomized linear algebra.

Education

Radoslav obtained his Bachelor’s degree in Mathematics from California State University, Monterey Bay. He then pursued his Master’s degree in pure mathematics under the guidance of Professor Serkan Hosten at San Francisco State University with focus on proving the Maximum likelihood degree of various toric varieties. After completing his Master’s degree, Radoslav enrolled in a Ph.D. program at the University of California, Merced. During his Ph.D., he conducted research on inverse problems formulated as PDE-constrained optimization, with focus on Hessian approximations. He graduated in 2022, under the supervision of Professor Noemi Petra. His work had applications in the field of ice sheet dynamics, particularly in the inversion of the basal sliding coefficient field for the Antarctic ice sheet.