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Statistical Machine Learning for Dynamic Network Modelling

Project description

Shaping decision-making with dynamic network modelling

The DyNeMo project aims to develop advanced statistical and machine learning models for understanding and predicting complex, evolving networks. These models will be applied to two unique datasets with the objective of resolving critical scientific queries that have social and environmental impact to help influence public policy. The first query focuses on how companies worldwide can optimise their socio-environmental impact in line with the United Nations sustainable development goals. The second assesses and predicts the behaviour of cascading pipe failures on water distribution systems. With the support of the Marie Skłodowska-Curie Actions programme, the DyNeMo project anticipates that the outcomes will offer significant predictions on socio-environmental impact to facilitate decision-making.

Objective

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.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

UNIVERSITY OF CYPRUS
Net EU contribution
€ 164 328,00
Address
AVENUE PANEPISTIMIOU 2109 AGLANTZI
1678 Nicosia
Cyprus
Region
Κύπρος Κύπρος Κύπρος
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data