Matteo Olivato

Research Topics

Deep Learning Models for Robotics, Cybersecurity and Innovative AI Applications

I’m working on Deep Learning models design for robotic and cybersecurity problems. The Deep Learning approach allows me to try to address more difficult cybersecurity tasks using only data, like: Anomaly Detection subdivided into Attacks and Faults Detection, Behavior Simulation and Behaviour Classification (es. Malware Type Classification etc.). A related research activity concerns the study and test of new Deep Learning methods as new Layer or Optimizers for better and faster models fitting on data without overfitting on them.


First year presentation

First year presentation


Matteo Olivato was born in Negrar (Verona, Italy), on Semptember 3th, 1993. He achieved his Master Degree in Information Engegneering in March 2018, in which he studied deep learning tecniques for automatic classification of attacks and faults on autonomous robots. More precisely in his Master Thesis he studied how to classify attacks on an autonomous boat (LUTRA) used in the INTCATCH Horizon2020 project ( Finally, he joined the “Artificial Intelligent” group (Supervisor: Alfonso Emilio Gerevini) of Information Engegneering Departement for research topics that concerns techniques for medical documents classification, anomaly detection for autonomous systems, general machine learning and deep learning applications on different human level problem resolutions