Giancarlo Valente

Assistant Professor


Machine learning and pattern recognition, supervised and unsupervised learning for neural data analysis

Parametric and non-parametric statistical assessment of classifiers results

MVPA design optimization


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.2017.09.032

Ontivero-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