Marc Vidal

PhD/MSc in Mathematical and Applied Statistics · PhD in Art Science

Research Associate, Ghent University   •   Guest researcher, Max Planck Institute for Human Cognitive and Brain Sciences

My research focuses on statistical methodology for high-dimensional and functional data, with particular interest in inference for latent structure. Some specific topics I work on include classification and clustering, covariance structure models, dimensionality reduction (including ICA), discrete functional data analysis, and moment-based operator methods. Applications in neurophysiological data (EEG, MEG, MRI, pupillometry) form a natural context for this work.

Publications (selected)

Noise-resilient penalty operators based on statistical differentiation schemes
Vidal, M., Y. Rosseel (2026)
Preprint arXiv
A family of moment operators for functional data and its discriminative properties
Vidal, M. (2025) In New Trends in Functional Statistics and Related Fields, eds. G. Aneiros, E.G. Bongiorno, A. Goia, and M. Hušková.
Contributions to Statistics, pp. 557–564. Springer, Cham. (IWFOS 2025) DOI
Functional independent component analysis by choice of norm: A framework for near-perfect classification
Vidal, M., M. Leman, A.M. Aguilera (2025)
Advances in Data Analysis and Classification arXiv DOI
A tutorial for understanding SEM using R: Where do all the numbers come from?
Rosseel, Y., M. Vidal (2025)
British Journal of Mathematical and Statistical Psychology 00: 1–38 DOI
Modeling emotional arousal with turbulence measured by EEG
Vidal, M., N. Moura, B. Van Kerrebroeck, A. M. Aguilera, T. H. Fritz, and M. Leman (2025)
Psychophysiology 62(6): e70093 DOI
© Marc Vidal – marc.vidalbadia@ugent.be