cv

PhD

• Currently working on cutting edge continual learning techniques and parameter efficient deep learning.
• Developed an embedded system for video anomaly detection on public buses to improve city safety.
• Designed a neural network for monocular depth estimation and contributed to People Tracking research.

MSc in Artificial Intelligence

BSc in Computer Science

Research Fellow

• Developed a deep learning based software for the detection of video anomalous activities on public transport for the pan-European project **InSecTT**.
• Studied and investigated techniques for data imbalance mitigation in anomaly detection problems.
• Introduced a novel consistency-based regulation loss for temporal anomaly localization in self-supervised learning settings.
• Developing of new techniques for video anomaly detection and video explaination using VAEs.

Head of Computer Division

• Construction of a dataset of traffic cones consisting of four classes and 9000 images, with which a neural network has been fine-tuned to perform cones detection in images.
• Developing of a deep learning based software for autonomous car perception based on cameras and Lidar sensors. The car is able to accurately detect the color and precise distance of traffic cones that define the track, in a 180° FOV ahead.
• Project manager for the computer division.

Intern

• Developed "ARPosture" (Swift) for the thesis work. The app was presented to the head of the Apple Developer Academy, being among the top 5 best apps developed at the University of Salerno in 2017-2019.