Giancarlo Valente
Assistant Professor, Maastricht University
giancarlo.valente@maastrichtuniversity.nl
https://www.maastrichtuniversity.nl/giancarlo.valente
RESEARCH INTERESTS
Statistical modeling
Machine learning and pattern recognition
Supervised and unsupervised learning for neural data analysis
Parametric and non-parametric statistics
Design optimization
KEY PUBLICATIONS
Raz, G., Svanera, M., Singer, N., Gilam, G., Cohen, M. B., Lin, T., Admon R,, Gonen T, Thaler A.,Granot R.Y.,Goebel R., Benini S., Valente, G. (2017). Robust inter-subject audiovisual decoding in functional magnetic resonance imaging using high-dimensional regression. Neuroimage, 163, 244-263. DOI: 10.1016/j.neuroimage.2017.09.032Ontivero-Ortega, M., Lage-Castellanos, A., Valente, G., Goebel, R., & Valdes-Sosa, M. (2017). Fast Gaussian Naïve Bayes for searchlight classification analysis. Neuroimage. DOI: 10.1016/j.neuroimage.2017.09.001
Santoro, R., Moerel, M., De Martino, F., Valente, G., Ugurbil, K., Yacoub, E., & Formisano, E. (2017). Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns. Proceedings of the National Academy of Sciences of the United States of America, 114(18), 4799-4804. DOI: 10.1073/pnas.1617622114
Valente, G., Castellanos, A. L., Vanacore, G., & Formisano, E. (2014). Multivariate linear regression of high-dimensional fMRI data with multiple target variables. Human Brain Mapping, 35(5), 2163-2177. DOI: 10.1002/hbm.22318
Valente, G., de Martino, F., Esposito, F., Goebel, R., & Formisano, E. (2011). Predicting subject-driven actions and sensory experience in a virtual world with Relevance Vector Machine Regression of fMRI data. Neuroimage, 56(2), 651-661. DOI: 10.1016/j.neuroimage.2010.09.062
Full list available at
https://scholar.google.nl/citations?user=rHrE6DQAAAAJ