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Tecnologo a tempo determinato

Istituto Nazionale di Fisica Nucleare

He works as a fixed-term technologist at the INFN Florence section, within the National Center 1 - ICSC National Research Center in High Performance Computing, Big Data and Quantum Computing, within Spoke 2 'Fundamental Research', WP2 'High Energy Physics' and WP6 'Industrial Applications & Space Economy'. He acts as Principal Investigator (PI) for the Flagship of 2.6.1. Fast Extended Computer Vision dedicated to distributed computing applications for the inference of neural networks dedicated to artificial vision on physical imaging. Collaborates within the WP2 Innovation Grant dedicated to Physics Informed Neural Networks applications on the LHCb project 'Flash simulations of resistive solid state detectors'. He collaborates as a member of the specific INFN initiative dedicated to artificial intelligence AI_INFN, where he also acts as a teacher for Hackathons.

• Deep Learning
• Cloud Computing
• Research
• Distributed Computing

Associato

ICSC - Centro Nazionale di Ricerca in HPC, Big Data e Quantum Computing

He works as a fixed-term technologist at the INFN Florence section, within the National Center 1 - ICSC National Research Center in High Performance Computing, Big Data and Quantum Computing, within Spoke 2 'Fundamental Research', WP2 'High Energy Physics' and WP6 'Industrial Applications & Space Economy'. He acts as Principal Investigator (PI) for the Flagship of 2.6.1. Fast Extended Computer Vision dedicated to distributed computing applications for the inference of neural networks dedicated to artificial vision on physical imaging. Collaborates within the WP2 Innovation Grant dedicated to Physics Informed Neural Networks applications on the LHCb project 'Flash simulations of resistive solid state detectors'. He collaborates as a member of the specific INFN initiative dedicated to artificial intelligence AI_INFN, where he also acts as a teacher for Hackathons.

• Deep Learning
• Cloud Computing
• Research
• Distributed Computing

Assegnista di Ricerca

Istituto Nazionale di Fisica Nucleare

I carried out ICT research and development of the SaaS platform of the organization's internal network dedicated to the applications of nuclear technologies on cultural heritage, INFN-CHNet (Cultural Heritage Network). I collavorated with colleagues from INFN-CNAF on the design, development with an IaC approach, and implementation of a PaaS-type architecture as a pilot for the European competence center for cultural heritage (activity carried out within the 4CH project). I actively collaborated in the integration and porting of the THESPIAN suite into the 4CH PaaS. Since 07/2022 I have been responsible for digital development and co-author of the AIRES-CH (Artificial Intelligence for digital REStoration of Cultural Heritage) project, written in collaboration with the PI Dr. Francesco Taccetti, and with the head of Scientific Analysis Dr. Chiara Ruberto. The AIRES-CH project was the winner of the Tuscany Region call Giovani Sì - FSC Research Grants Year 2021, CUP I95F21001120008. The AIRES-CH project focuses on the development of a native cloud application based on deep neural networks to perform the task of automatic color assignment to imaging analyzes with nuclear technologies, in particular X-ray fluorescence analysis. I personally followed the design, writing, training and testing of neural networks, as well as data recovery, dataset creation and data engineering. The code for the development of the neural networks was written in Python, exploiting packages for numerical calculation such as NumPy and SciPy, with the TensorFlow and Keras packages for the architectures, and with the Optuna package for the optimization of the hyperparameters through the Bayesian optimization. Finally I worked on the design, development and release of a server application with REST API architecture to serve trained neural networks via the web. The code for the REST-API is released on GitHub.

• Deep Learning
• Cloud Computing
• Computer Vision

Assegnista di Ricerca

Istituto Nazionale di Fisica Nucleare

I deal with research and development of digital infrastructures and web services for the Digital Heritage Laboratory (DHLab) of the Cultural Heritage Network (CHNet), National Institute of Nuclear Physics, Florence, within the European Open Science Cloud (EOSC) European research projects ) - Pillar (https://www.eosc-pillar.eu/), and ARIADNEplus (https://ariadne-infrastructure.eu/).

• Deep Learning
• Cloud Computing
• Computer Vision
• Full-stack development

PhD, Physics

University of Padova

Doctor Europaeus certificate. Vote: Excellent

MSc, Scienze Fisiche e Astrofisiche

University of Florence

Physics (LM-17). Vote: 110 e Lode

BSc, Fisica e Astrofisica

University of Florence

Physics (L-30). Vote: 110 e Lode

Extended Computer Vision at high rate - ICSC Spoke 2 WP6 Flagship 2.6.1

The flagship use case focuses on two aspects related to applied research in domains using computer vision, coupled with the need to perform fast (“high rate”) analyses on large real / realistic datasets. From the technological point of view, one of the purposes of the presented use case, is to build a demonstrator to validate the high rate analysis infrastructure provided by WP5 has the capability to support a wide range of different scientific domains. The proposed infrastructure aims to support the paradigm shift from a batch-based approach to an interactive analysis model based on a parallel processing possibly over geographically distributed backends.

• Distributed Computing
• Nuclear Vision
• Deep Learning
• Spectral Vision

Artificial Intelligence for digital REStoration of Cultural Heritage - AIRES-CH

• Nuclear Vision
• X-Ray Fluorescence
• Deep Learning