Statistical Machine Learning for Dynamic Network Modelling

Descrizione del progetto

La modellizzazione dinamica delle reti per dare forma al processo decisionale

Il progetto DyNeMo punta a sviluppare modelli statistici e di apprendimento automatico avanzati per la comprensione e la previsione di reti complesse e in evoluzione, che saranno applicati a due insiemi di dati unici con l’obiettivo di risolvere quesiti scientifici critici in grado di esercitare un impatto sociale e ambientale per contribuire a influenzare le politiche pubbliche. Mentre il primo si concentra sulle modalità in cui le aziende di tutto il mondo possono ottimizzare il loro impatto socio-ambientale allineandosi agli obiettivi di sviluppo sostenibile delle Nazioni Unite, il secondo valuta e prevede il comportamento dei guasti a cascata delle tubazioni nei sistemi di distribuzione idrica. Sostenuto dal programma di azioni Marie Skłodowska-Curie, il progetto DyNeMo prevede che i suoi risultati offriranno previsioni significative sull’impatto socio-ambientale per facilitare il processo decisionale.

Obiettivo

Networks are ubiquitous in the society, technology, biology and economy. Being able to perform accurate inference and prediction on such data is highly non-trivial due to their large size and complex dynamic behaviour. In many networks such as social ones, connectivity patterns and network structurechange dynamically. On the other hand, technological networks such as water systems have fixed structure but dynamic processes might take place on them appear, such as failures. DyNeMo is a cross-disciplinary project that aims to provide a novel, explainable yet scalable framework using statistical machine learning to model dynamic behavior on networks, with the goal to answer crucial scientific questions with social and environmental impact, influencing public policy. The project combines statistical tools that ensure flexibility, data adaptivity and interpretable parameters with Deep Learning tools ensuring efficiency and scalability. The methods will be used to answer two highly impactful scientific questions given access on unique datasets. The first is how companies worldwide can maximize their socio-environmental impact aligned with UN sustainable goal strategies. The second question is how to quantify and predict behaviour of cascading pipe failures on the water distribution systems in Cyprus. The results will detect patterns and perform prediction, thus influencing public policy in alignment to the EU strategic goals on monitoring water systems. The fellow and the supervisor have complementary expertise which in combination with the plan, the arrangements and environment provided by the host institution ensure a successful implementation of this novel and timely project as well as an effective dissemination and utilisation of the expected outcomes. The results of the proposed action are expected to have important conclusions on socio-environmental impact and can shape decision making and policy making in the EU and around the world.