The Team
Davide, Jemima, and Nick have pairwise intersecting expertise on mathematics of data science, optimization, numerical linear algebra, and the solution of differential equations, thereby ensuring a common vocabulary and viewpoints. They also bring their own complementary expertise on efficient numerical linear algebra for partial differential equations, optimal control problems, and low-rank tensor decompositions, which they will integrate in TOC4Deep.
Davide Palitta
PI
Dr. Davide Palitta is a junior assistant professor at the Department of Mathematics, University of Bologna, where he obtained his PhD in 2018. His main research interests concern the design of a proper linear algebra phase involved in the numerical solution of elliptic and evolutionary PDEs. In particular, he has experience with diverse discretization schemes and the full exploitation of their properties in the devising of efficient linear solvers and preconditioners.
Jemima Tabeart
Dr. Jemima Tabeart is a postdoctoral research associate at the University of Edinburgh. She obtained her PhD in 2019 from the University of Reading. She has expertise in developing new preconditioners for high-dimensional saddle point problems arising from applications. Her current research focuses on PDE-constrained optimization problems, and many of the methods she uses are immediately applicable to this project. In addition to theoretical advances, she has significant experience working directly with industry collaborators at the Met Office, UK. Her previous work on data assimilation that combines data-driven online approaches with mathematical techniques is now used at national meteorological centers.
Nick Vannieuwenhoven
Dr. Nick Vannieuwenhoven is an assistant professor in Numerical Techniques and Data Science at KU Leuven. He obtained his PhD from KU Leuven in 2015. His research interests include numerical multilinear algebra, tensor decompositions, numerical analysis, applied differential geometry, and Riemannian optimization. He supervises a PhD project on tensor decompositions as a bias in the parameter space of deep learning. He serves as the exchange coordinator for the Master in Mathematical Engineering.