Research Interest

My primary research field is unsupervised learning and data science, particularly in the field of complex systems dynamics. My goal is to build out a theory to provide new approaches to clustering and anomaly detection by applying topological methods (TDA) thus providing a new approach to Topology-based unsupervised learning.

My application domain mainly concerns the medical and biological field (in my PhD thesis I focused on the study of epilepsy and on the analysis of EEG traces). I especially analyze data represented as time series.

I am also interested in more purely educational aspects and I participated in the Erasmus+ Project, Da.Re, where a European curriculum was proposed for a master’s degree in Data Science.


A full list of publications is available at the above Google ReserchGate link.