Co-chairs: Eugen Pircalabelu and Cristian Gatu.

Description: The aim will be to contribute to this crucial big data problem that genuinely arises when handling complex data, especially in the case of text data and some approaches to functional data. Common topics, such as model/variable selection which relies on proper methods for dimension reduction, sparsity, and estimation in high- and ultra-high-dimensions will be approached in generalized spaces taking inspiration from powerful computational techniques.

Main tasks- To analyze high dimensionality in Hilbert spaces based on regularization, methodologies based on linear operators, L2-type metrics, and (non-)convex (constrained) optimization in Hilbert spaces, best subset model and feature selection procedures in high dimensions. To connect high-dimensionality research with the achievement of WG 1 and the implementations to be carried out for WG 4.