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List of key projects I participated in (see the CV for the complete list):
Investigating coronal holes’ shapes and their relationship with high-speed solar wind streams via topological data analysis [2025-2027]
Investigating coronal holes’ shapes and their relationship with high-speed solar wind streams via topological data analysis [2025-2027]
Role: Principal Investigator
Granting institution: Istituto Nazionale di Astrofisica (INAF)
Funding: 20000 Euro
Coronal holes are regions of open magnetic field from which the fast solar wind originates. Monitoring the onset of equatorial coronal holes, classifying them according to their shape and magnetic geometry, is crucial in space weather forecasting and in providing insights into how these structures form and their magnetic configuration is modulated by the emergence of the photospheric magnetic field. Moreover, quantifying how solar wind speed is influenced by the coronal hole configuration is important in establishing how the microphysical properties of the plasma are linked to large-scale source regions. This project aims to address this topic, exploiting multi-spacecraft and multi-instrument solar observations, and advanced machine learning and mathematical topology-based techniques.
Coronal holes.
Left: an image from SDO/AIA where a large coronal hole is visible.
Right: the coronal hole cropped from the full image.
Left: an image from SDO/AIA where a large coronal hole is visible.
Right: the coronal hole cropped from the full image.
Persistence diagrams.
Left: 0-th dimensional persistence diagram for the cropped coronal hole's image.
Right: 1-st dimensional persistence diagram for the cropped coronal hole's image.
Left: 0-th dimensional persistence diagram for the cropped coronal hole's image.
Right: 1-st dimensional persistence diagram for the cropped coronal hole's image.
p-Laplacians on hypergraphs and machine learning for leak detection in water distribution systems (HyperAq) [2022-2024]
p-Laplacians on hypergraphs and machine learning for leak detection in water distribution systems (HyperAq) [2022-2024]
Role: Participant (Jan 2022 - Feb 2024), Scientific Director (Mar 2024 - Dec 2024)
Granting institution: Ministero dell'Università e della Ricerca (MUR) PON Ricerca e Innovazione, 2F Water Venture company
Funding: 150000 Euro (cost of my three-year research contract)
PI: prof. Mario Putti (Jan 2022 - Feb 2024)
In this project, the aim is to model the aqueduct's hydraulic behavior using a p-Laplace equation defined on the graph. Indeed, an acqueduct can be represented as a graph consisting of pipes (graph edges) and junctions (graph nodes), and the p-Laplacian operator is involved in the nonlinear partial differential equation that can be derived from the physics laws underpinning the hydraulic dynamics. This p-Laplacian based model, tailored to the behavior of individual aqueducts, acts as a surrogate model for an operational water distribution system, whose accurate description after years is made unfeasible by inevitable, and almost undetectable, aging and degradation due to the passage of time. This surrogate model is empowered by machine learning tools, and capable of well modeling an operational acqueduct, including the presence of water leaks.
(β,γ)-Lissajous points and curves of the square [2021-2022]
(β,γ)-Lissajous points and curves of the square [2021-2022]
Role: Principal Investigator
Granting institution: Gruppo Nazionale per il Calcolo Scientifico (GNCS) - Istituto Nazionale di Alta Matematica (INdAM)
Funding: 1160 Euro
In this INdAM grant for young researchers, the aim is to investigate on the extension of the (β,γ)-Lissajous points from the interval to the square, in order to use them in the framework of multivariate polynomial interpolation. Moreover, related Lissajous curves are also considered. The funding is designated for the participation in the conference Curves and Surfaces 2022 in Archachon, France, where the results of the project will be discussed.
Artificial Intelligence for the analysis of solar Flares data (AI-FLARES) [2020-2022]
Artificial Intelligence for the analysis of solar Flares data (AI-FLARES) [2020-2022]
Role: Participant
Granting institution: Agenzia Spaziale Italiana (ASI) - Istituto Nazionale di Astrofisica (INAF)
Funding: 162839 Euro (cost of my research grant + travels: 39066 Euro)
PI: prof. Michele Piana (WP leader: prof. Cristina Campi)
AI-FLARES (Artificial Intelligence for the Analysis of Solar Flares Data) is a research project funded by the Agenzia Spaziale Italiana and by the Istituto Nazionale di Astrofisica within the framework of the “Attività di Studio per la Comunità Scientifica Nazionale Sole, Sistema Solare ed Esopianeti” program. The topic addressed by this project is the development and use of computational methods for the analysis of remote sensing space data associated to solar flare emission.
Left: an image from HMI, onboard SDO.
Right: two videos obtained from HMI data. Predicting the magnitude of a occurring flare (in this example, above C class or not) is a non-trivial task.
Right: two videos obtained from HMI data. Predicting the magnitude of a occurring flare (in this example, above C class or not) is a non-trivial task.
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