Daniel Cortild

Daniel Cortild is a Research Fellow in the Department of Mathematics at the University of Oxford. His research focuses on Machine Learning and Data Science, as well as Numerical Analysis. Cortild is affiliated with the Mathematical Institute at the University of Oxford, located in the Andrew Wiles Building, Radcliffe Observatory Quarter. He is currently a postgraduate student and has contributed to recent publications in areas such as Krasnoselskii–Mann iterations and global optimization algorithms. His work appears in journals such as the Journal of Optimization Theory and Applications and Transactions on Machine Learning Research.

Daniel Cortild is a researcher at the University of Oxford, specializing in Optimization and Mathematical programming. His work focuses on developing and analyzing algorithms for solving complex optimization problems, with an emphasis on stochastic methods and their convergence properties. Cortild's research includes the study of iterative algorithms such as Krasnoselskii–Mann iterations, and their applications in areas like stochastic optimization and differential privacy. His publications explore topics such as last-iterate complexity, bias-optimal bounds for SGD, and regularization methods for variational inequalities. His contributions highlight the intersection of optimization theory with computational methods and privacy-preserving techniques.