After obtaining my Bachelor and Master degrees working on Signal Processing, Computer Vision and Machine Learning, since the second year of my PhD I became more and more interested in neuroscience, thanks to multiple collaborations with different Brain centres (Maastricht and Tel Aviv). Wandering across Cognitive Neuroscience and Artificial Intelligence (specifically deep learning), I am interested to address the goal to understand the brain activities in relation with movie stimulus applying recent advances in machine learning. I joined Prof. Lars Muckli’s lab since May 2017, as postdoctoral researcher, to extend my studies also to high-field fMRI and localisation of non-feedforward sources in primary visual cortex.
Svanera M., S. Benini, G. Raz, T. Hendler, R. Goebel, and G. Valente. “Deep driven fMRI decoding of visual categories”. In: NIPS Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI, 2016). url: https: //arxiv.org/abs/1701.02133.
Benini, S., M. Svanera, N. Adami, R. Leonardi, and K.A. Bálint. “Shot Scale Distribution in Art Films”. In: Multimedia Tools and Applications, 2016. doi: 10.1007/s11042- 016-3339-9.
Gordiychuk, A., M. Svanera, S. Benini, and P. Poesio. “Size distribution of micro bubbles for a venturi type bubble generator: effect of different parameters on bubble mean size, statistics of the distribution”. In: Experimental Thermal and Fluid Science, 2016. doi: 10.1016/j.expthermflusci.2015.08.014.
Svanera M., U. Riaz Muhammad, R. Leonardi, and S. Benini. “Figaro, hair detection and segmentation in the wild”. In: Proceedings of IEEE International Conference on Image Processing (ICIP, 2016). doi: 10.1109/ICIP.2016.7532494.
Svanera M., S. Benini, N. Adami, R. Leonardi, and K.A. Bálint. “Over-the-Shoulder Shot Detection in Art Films”. In: 13th International Workshop on Content-Based Multimedia Indexing (CBMI, 2015). doi: 10.1109/CBMI.2015.7153627.
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