Other international projects
Department of Environmental Sciences, Informatics and Statistics

Natural hazards like river floods and water browning caused by organic matter washed into aquatic environments are becoming more intense because of climate change. Nature-based solutions such as peatland restoration are often proposed as measures that may help reduce these risks and adapt to climate change. However, the knowledge about the effects of peatland restoration on floods and water browning is limited. The ANTIPASTI project clarifies how peatland restoration modifies the way landscapes retain, transform and release water and biogeochemical elements , and how it affects their capability of modulating floods and aquatic carbon fluxes causing water browning. We measure in the field how quickly discharge of streams reacts to precipitation events and how old is the water released from drained and restored peatlands (a proxy for water quality). These new tools are used to understand interactions between climate change and peatland restoration, assess the effects of this nature-based solution for river floods and water browning, and unveil possible emerging risks. The vision of ANTIPASTI is to create knowledge and tools to enable landscape restoration specifically designed to achieve flood hazard reduction and water quality targets.

The HEALTH-AI project aims to bridge the gap between healthcare and Artificial Intelligence (AI) by providing professionals and students with the knowledge, skills, and ethical awareness necessary for the safe and effective integration of AI into clinical practice. It aims to improve AI skills through an adaptive AI curriculum, strengthen interdisciplinary collaboration and knowledge sharing between informatics scientists and healthcare professionals through a co-creation platform, and promote responsible AI adoption through appropriate ethical guidelines.

BRIDGE is a collaborative project funded by the Ministry of Education, Universities and Research (MIUR) involving Ca' Foscari University of Venice, the Polytechnic University of Turin, the University of Udine, Addis Ababa University, Jimme University, and INRS Montréal. The overall objective of the project is to establish a collaborative network among the partner universities, including both the exchange of best practices in adopting methods and tools for the digital transition of universities and the development of research projects. Specifically, the activities will be aimed at university managers, directors, and research group leaders focused on digitalization and the adoption of artificial intelligence tools to improve management processes and research quality governance. The activities will also involve university professors, researchers, and doctoral students in a multilateral capacity-building effort on advanced materials and technologies for energy and environmental applications.

The EcoDigify project aims to build university-level training courses of an interdisciplinary nature to promote sustainability in the use and development of digital technologies. This will be achieved by providing teachers with materials and tools on sustainable digitalisation and providing students with the knowledge and skills to create sustainable digital solutions with the support of experts and researchers working in the field of sustainability.

ATLAS aims to bring about positive change for local authorities and stakeholders in the assessment and management of the relationship between green spaces and historic buildings. The project proposes an approach to risk analysis with the aim of developing innovative tools that facilitate the assessment of the multiple hazards, vulnerabilities and risks associated with the conservation of heritage and green spaces in historic cities, while ensuring careful use of public funds. In addition, protocols will be established to promote the implementation of sustainable adaptation and mitigation strategies based on integrated and participatory management of green spaces and heritage resources.

AWS IAM access control policies specify what AWS cloud resources are accessible by what identities. AWS SDKs allow developers to programmatically access these resources through applications that can be run by different identities. However, what resources are accessed and how depend on the (mostly string) values computed by the application. Therefore, forecasting what access control policy is required by a given application is rather complex. In this project, we apply abstract interpretation techniques to over approximate the string values computed by these applications, and then infer, validate or IAM the access control policies.