Bio Summary:
Leo D’Amato is a Ph.D. researcher in Artificial Intelligence based in Pescara, Italy. Currently enrolled at ISTC-CNR, the Institute for Cognitive Sciences and Technologies of the Italian National Research Council, he is a first-cycle participant in the Italian National Ph.D. Program in Artificial Intelligence. With a passion for multi-agent systems and deep cognitive skills, Leo’s research spans various applications, including a significant role in the SORTEDMOBILITY project—an H2020 initiative focused on developing a self-organized and decentralized rail traffic management system.
Education:
Ph.D. in Artificial Intelligence (2021 – Today):
Italian National Research Council (CNR), Italy.
Master’s Degree in Mathematics (LM-40) (2018 – 2020):
Université Côte d’Azur and University of L’Aquila, France.
Thesis: Mean Field Games on finite state space.
Bachelor’s Degree in Mathematics (L-35) (2015 – 2018):
University of L’Aquila, Italy.
Thesis: Random Walk and Heat Equation.
Research Focus:
Leo’s primary research focus lies in multi-agent systems with an emphasis on endowing agents with deep cognitive skills. His work explores perception, learning, and planning within these systems. Leo actively contributes to the SORTEDMOBILITY project, showcasing the practical application of his research in developing a self-organized and decentralized rail traffic management system.
Professional Journey:
Lecturer (Feb 2022):
Talent Garden Innovation School, Milano, Italy.
Lectured on the Statistics module for the Business Data Analysis Master.
Data Scientist (Apr 2021 – Oct 2021):
Linkalab s.r.l., Cagliari, Italy.
Applied natural language processing techniques for real-time reputation monitoring on social networks.
Data Scientist (Feb 2021 – Mar 2021):
Fater s.p.a., Pescara, Italy.
Developed algorithms for efficient planning of promotional activities.
Honors & Awards:
Master’s Degree (2018 – 2020):
Graduated with honors (110/110 cum laude).
Publications Top Noted & Contributions:
Leo has made significant contributions to the field of Artificial Intelligence, particularly in multi-agent systems. Specific publications and contributions can be referenced through his professional profiles.
“Neural representation in active inference: using generative models to interact with–and understand–the lived world“
- Authors: G Pezzulo, L D’Amato, F Mannella, M Priorelli, T Van de Maele, …
- Publication: arXiv preprint arXiv:2310.14810
- Year: 2023
“Onset time detection of acoustic emission signals for structural monitoring with deep learning“
- Authors: J Melchiorre, F Agostini, L D’Amato, M Rosso
- Publication: Titolo volume non avvalorato
- Year: 2023
“Self-organization for train re-scheduling and re-routing: a proof of concept“
- Authors: L D’Amato, F Naldini, V Tibaldo, V Trianni, P Pellegrini
- Publication: ROADEF 2023-24ème édition du congrès annuel de la Société Française de …
- Year: 2023
“A consensus algorithm for decentralized real-time railway traffic management“
- Authors: L D’Amato, V Trianni, P Pellegrini
- Publication: ODS2023 e–book of abstracts
- Year: 2023
“Designing Self-Organizing Railway Traffic Management“
- Authors: L D’Amato, F Naldini, V Tibaldo, V Trianni, P Pellegrini
- Availability: Available at SSRN 4451299
Research Timeline:
Ph.D. Research (2021 – Today):
Focused on multi-agent systems and deep cognitive skills.
Active involvement in the SORTEDMOBILITY project.
Master’s Research (2018 – 2020):
Explored Mean Field Games on finite state space.
Bachelor’s Research (2015 – 2018):
Investigated Random Walk and Heat Equation.
Leo D’Amato’s academic and professional journey reflects a commitment to advancing the field of Artificial Intelligence, with notable contributions to multi-agent systems and real-world applications. His educational achievements and practical experiences position him as a dedicated and skilled researcher in the AI domain.
