We consider a linear regression problem in a high dimensional setting where the number of
covariates p can be much larger than the sample size n. In such a situation, one often …

Current high-throughput data acquisition technologies probe dynamical systems with
different imaging modalities, generating massive data sets at different spatial and temporal …

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in
Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and …

The ability to accurately infer functional connectivity between ensemble neurons using
experimentally acquired spike train data is currently an important research objective in …

The overall aim of this thesis is the development of novel electroencephalography (EEG)
and magnetoencephalography (MEG) analysis methods to provide new insights to the …

The dissemination of software has never been so easy since the Internet became widely
available. This ease of access to free software has also pushed the wide spread of viruses to …

The perplexing effects of noise and high feature dimensionality greatly complicate functional
magnetic resonance imaging (fMRI) classification. In this paper, we present a novel …

With recent development of high-throughput genoty** technologies, the usage of Genome-
Wide Association Studies (GWAS) has become widespread in genetic research. By …

The curse of dimensionality constitutes a major challenge to functional magnetic resonance
imaging (fMRI) classification. Coupled with the typically strong noise in fMRI data, prediction …

Conformal predictors, introduced by Vovk et al.(Algorithmic Learning in a Random World,
Springer, New York, 2005), serve to build prediction intervals by exploiting a notion of …