• Conferenza GARR 2018

     

     

       GARR Conference is the annual meeting of users, operators and managers of the Italian national education and research network aimed at sharing experiences and comments on the use of the network as a tool for research, training and culture, in different contexts and disciplines.
   

     

     

Temi della conferenza    

Temi della conferenza    

The core themes of this editions are data, artificial intelligence and technology transfer both towards the enterprises and single persons. We will talk about open data and services, cybersecurity, industry 4.0 and its relations with research and innovation. We will discuss how education should be rethought in order to keep the pace with the continuous evolution of ICTs, also with the help of these very same technologies.   

Programme


Corsi di formazione   

Corsi di formazione   

On 1-2 October there will be several training opportunities for GARR user community. These courses span from classes dedicated to trainers (Moodle: how to manage a course e How to create and disseminate educational material online), to courses on Public Speaking, on cybersecurity (PenTest & Rooting) and on networking (Software Defined Network). For registrations to these training courses, go to the Learning GARR platform.    

Programme

3 ottobre 2018

Angelo Mariano

ENEA
https://www.enea.it/

Qualità dell'aria: integrazione del machine learning con il modello fisico per l'analisi di un grande dataset distribuito

Dottore di ricerca in fisica teorica, attualmente ricercatore ICT in ENEA, ha iniziato a lavorare come software developer e analista di sistemi, acquisendo gradatamente competenze nel campo delle progettazione informatica, dell'integrazione tra i sistemi ICT, dell'elearning e della formazione blended, del calcolo scientifico ad alte prestazioni, del cloud computing. Attualmente si occupa di informatica gestionale, gestione dei processi aziendali e dei flussi di dati. Appassionato di machine learning e deep learning, alla ricerca dei sistemi più efficienti per integrare l'intelligenza artificiale nella vita quotidiana di imprese e istituzioni.

PhD in theoretical physics, currently ICT researcher in ENEA, he started working as a software developer and system analyst, gradually acquiring skills in the field of computer design, integration between ICT systems, e-learning and blended training, of high performance scientific computing, cloud computing. Currently he deals with IT for the management of business processes and data flows. He is deeply interested in machine learning and deep learning, always in search of the most efficient systems to integrate artificial intelligence into the daily life of companies and institutions.

SESSIONE 4. CYBERSECURITY e AI

Qualità dell'aria: integrazione del machine learning con il modello fisico per l'analisi di un grande dataset distribuito

Il presente studio si basa sulla simulazione eseguita con AMS-MINNI (Atmospheric Modeling System of MINNI) per l'anno 2010 e il suo scopo è utilizzare il machine learning e il deep learning per estrarre caratteristiche rilevanti e trovare idee dall'enorme set di dati utilizzato e prodotto dalla simulazione raccolta e conservata nei cluster distribuiti di ENEA CRESCO.


Air pollution: integration of physical model and machine learning to analyze a large distributed dataset

The present study is based on the simulation carried out with AMS-MINNI (Atmospheric Modelling System of MINNI) for the year 2010 and its purpose is to use machine learning and deep neural networks to extract relevant features and find insights from the huge dataset used and produced by the simulation collected and stored on ENEA CRESCO distributed clusters.

 


Sponsor

DELL EMC
Palo Alto Networks
INTEL

juniper networks
maticmind