My primary research interests are in the area of pattern recognition and machine learning. Within these areas, my work focuses on developing novel strategies to formalize, explain and visualize the pattern in data. My work encompasses different case studies. I worked on many aspects of feature selection in machine learning introducing the concept of graph-based feature selection. Such a framework is prone to parallelization making this family of algorithms highly scalable (i.e., suitable for Big Data analysis). Another important aspect I dealt with is the real-time factor of such techniques. This framework turns out to be also suitable for visual object tracking, demonstrating to improve tracking performance while maintaining high frame rates. Finally, I have also worked on soft-biometrics by taking into account user-centric aspects, such as personality.