My main research activity focuses on the application of deep learning techniques (CNN, RNN, GAN) to several practical use cases, from medical image analysis to EEG processing to document classification. During my Ph.D. I have studied and compared different human-computer interaction modalities and related methods for integrating human feedback into automatic computer vision methods, both based on deep learning and not. In the past, I have also worked with video object segmentation and tracking for underwater scenarios.
I have a strong programming background (learned on my own) and a good mathematical background (thanks to my engineering studies), which usually take me to a "hands-on" approach when studying and experimenting with new research findings and ideas.
Work aside, I am very passionate about football (the round-ball one), music (preferably with a couple of electric guitars, bass and drums, but I'm not a fundamentalist) and books (mostly 14-volume-long fantasy sagas, lately).

Ph.D. in Computer Engineering (2016)

Thesis: "Hybrid human-machine vision systems for automated object segmentation and categorization"

Master's degree in Computer Engineering (2010)

Bachelor's degree in Computer Engineering (2007)