About
I am an Automation Engineer working as a Researcher at the Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), in the National Research Council of Italy (CNR). I received my master's degree in Automation Engineering, cum laude, in 2019, from the Polytechnic of Bari, Italy. From 2020 to 2023, I was a Ph.D. student in Smart and Sustainable Industry at the Polytechnic of Bari, Italy. From March 2022 to September 2022, I carried out my research activity within the "Computer Vision Group" at the Institut für Informatik of the Rheinische-Friedrich-Wilhems-Universität Bonn, Germany. From October 2019 to December 2023, I was a Research Fellow at the STIIMA Institute of the CNR in Bari, Italy, where I was involved in several scientific projects and works concerning computer vision, artificial intelligence, and the implementation of robotic systems using ROS. Since September 2024, I have held the position of Contract Professor at the Polytechnic of Bari, Italy, where I teach Informatics for Engineering.
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Since December 2023, I have held the position of Staff Researcher at the STIIMA Institute of the CNR in Bari, Italy, where I am part of the Intelligent, Sensing, and Perception (ISP) research group. I am involved in the study of intelligent devices and systems for monitoring human movements and interactions, aimed at the realization of computer vision, machine learning, and deep learning applications that assist people in various fields, from healthcare to industrial environments. I also work in the area of smart agriculture, from robotics to systems for semantic and instance segmentation of natural images. I have authored scientific papers in international peer-reviewed journals and proceedings of international conferences.
Research Interests
The research activity is focused on the analysis of vision systems for monitoring the interactions of humans with environment, machines, and artificial intelligence systems. Data acquisition campaigns were carried out through multimodal systems, and these data were used to extract RGB, Depth and point cloud information. Implementation of machine learning and deep learning algorithms to be towed on the extracted data to realize autonomous systems capable of performing predictions, classifications, and diagnostics. Projects in human monitoring have been followed.
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Implementation of an RGB-D camera setup for 3D modelling of a work cell, in which the operator works closely with a collaborative robot. Calibration of such cameras to process multimodal data on a single reference system. Implementation of different deep learning models for segmentation of actions performed by operators during an assembly task, with the goal of giving this information to the collaborative robot. The aim is to be able to fully adapt to the operator, thus focusing on the physical and cognitive well-being of the human being, compatible with the human-machine jobs typical of Industry 5.0.
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Development of deep learning methods for fall risk recognition of patients with neurodegenerative diseases. The deep learning model was trained using features extracted from video data consisting of patients engaged in performing specific exercises, followed by the physical therapist. These features contain a system of representing the individual according to a simplified group of points, called skeleton, which allows the identification and tracking of joints such as shoulders, knees, elbows, and hands.
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Definition of RGB-D tools for natural image acquisition, focusing the study on semantic segmentation of various types of grapes in vineyards by integrating RGB and Depth information. Methods for semantic segmentation of each individual leaf were realized, with the aim of calculating the surface area.
Education
2020 - 2023
Ph.D. in Smart and Sustainable Industry
Polytechnic of Bari, Italy
Thesis: Vision devices and intelligent systems for monitoring the well-being of humans in healthcare and manufacturing
Date of Graduation: 11/01/2024
The research project for the Ph.D. course aims to analyze the cognitive aspect and well-being of operators in companies that, following the paradigms dictated by Industry 4.0, adopt collaborative robots in their production lines. Intelligent devices and systems for monitoring movements and interactions between operators and cobots are studied. In general, the study of such systems is aimed at creating comfortable working environments, adaptable to the needs and abilities of individuals, increasing the well-being of operators in a human-centered factory concept.
2017 - 2019
Automation Engineering
Polytechnic of Bari, Italy
Thesis: Design and experimentation of a robotic system for the automatic installation of an Internet of Things network in outdoor scenarios
Final Vote: 110/110 with honours
Date of Graduation: 25/07/2019
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During the thesis period, an internship was conducted at the Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA) of the National Research Council (CNR). The automated deployment of a set of sensor nodes in an outdoor scenario, using an Unmanned Ground Vehicle, was implemented to achieve a well-distributed IoT network architecture, implementing an algorithm for proper outdoor localization.