Machine Learning for Automated Seabed Mapping
Ital-IA - Thematic Workshop, 2024
We present a novel machine learning framework for the automated classification and segmentation of seabed morphologies using multibeam echosounder data.
Hi, my name is
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I'm a Data Scientist and Physicist with a passion for applying machine learning to real-world problems. I hold a PhD in Data Science & Artificial Intelligence from the University of Trieste.
I am a researcher and data scientist with a solid background in Physics and a PhD in Data Science & Artificial Intelligence.
I work on machine learning and mathematical modeling, combining theoretical thinking with practical Python implementation to solve complex, data-driven problems. My work includes GIS-based marine seabed analysis using ML/DL for image classification and semantic segmentation, as well as experience with Physics-Informed Neural Networks (PINNs) for differential equation modeling.
University of Trieste, Italy
Thesis: "Machine Learning for Automated Recognition of Seabed Morphologies"
Research focus: developed novel machine learning algorithms for the classification of seabed morphologies using multibeam echosounder data, achieving state-of-the-art performance and providing new insights into underwater geological processes.
Read the PhD thesisAlma Mater Studiorum - University of Bologna, Italy
Thesis: "Deconfined Quantum Criticality"
Grade: 110/110
Read the Master's thesisAlma Mater Studiorum - University of Bologna, Italy
Thesis: "Statistica Frazionaria e Anioni"
Grade: 110/110
Read the Bachelor's thesisItal-IA - Thematic Workshop, 2024
We present a novel machine learning framework for the automated classification and segmentation of seabed morphologies using multibeam echosounder data.
Ital-IA - Thematic Workshop, 2023
A review of machine learning applications in atmospheric, oceanic, and seabed data analysis.
37th International Geological Congress, 25–31 August 2024, Busan, South Korea
Geosciences and Information Technologies, 16–18 June 2025, Milazzo, Italy
This repository provides a personal wallet manager application with a web-app interface (HTML/JS/CSS), a Telegram bot interface, and a graphical user interface (GUI) with Tkinter. The application allows users to add their expenses categorized into different types and generate statistical reports, such as bar and pie charts, for various time periods.
Interested in collaborating, have questions about my research, or just want to chat? Feel free to reach out!