Coralia Cartis

Coralia Cartis is a Research Fellow in the Department of Mathematics at the University of Oxford. Her research interests include algorithm design, analysis, and implementation for linear and nonlinear optimization, convex and nonconvex problems, and large-scale optimization complexity. She explores interconnections between dynamical systems and continuous optimization, with applications in compressed sensing, sparse approximation, machine learning, and inverse problems in climate modelling. Cartis is affiliated with the Mathematical Institute and holds a Turing Fellowship at The Alan Turing Institute. Her work has been published in several journals, including SIAM Journal on Optimization and Mathematical Programming.

Coralia Cartis is a researcher at the University of Oxford with expertise in Optimization, Numerical Analysis, Complexity, and Compressed Sensing. Her work focuses on developing and analyzing algorithms for nonconvex optimization, including adaptive cubic regularization methods, derivative-free optimization, and trust-region approaches. She has contributed to understanding the convergence rates and complexity of optimization algorithms, particularly in the context of nonconvex and constrained problems. Her research also explores the theoretical foundations of compressed sensing and its implications for signal recovery. Cartis' publications highlight the interplay between algorithmic design and computational complexity in optimization and numerical analysis.