My main interest is to characterize the anatomo-functional architecture of individual brains using neuroimaging data, and in particular using resting-state fMRI. I am also interested in examining how brain connectivity can be used as a biomarker of neurodegenerative diseases such as Alzheimer's disease. These questions raise considerable methodological challenges, which feed the technical aspects of my work. To explore the resting-state networks in fMRI, I use some unsupervised pattern recognition techniques, i.e. various types of clustering and component analysis. To deal with the statistics associated with a stochastic clustering process, I have been working on non-parametric statistical methods, in particular based on the bootstrap. Besides the exploration of real data, my research also includes the development of fully synthetic neuroimaging databases which cover many aspects of the data-generating process, from neural activity and physiological noise to the physics of image acquisition, to provide a test bed for the evaluation and validation of neuroimaging analysis methods.

Globalization is bringing many changes to the positions and skills required of institutions and individuals. In this context of constant change, workplace training is becoming increasingly important. But it must be recognized that training programs do not always produce concrete changes in workplace behaviour. My research program revolves around this issue of transfer of learning. Starting from theories and models concerning changes in behaviour, it identifies the parameters that influence and enhance transfer of learning. The research mainly takes place in the actual workplace and involves descriptive or causal longitudinal data collection. The research methodologies are sometimes experimental, sometimes descriptive and invariably call for the use of sophisticated statistical analysis techniques and methods.