Projects
HUmLrn
The HUmLrn project explores how AI agents can effectively utilize diverse human feedback to enhance their learning capabilities. Unlike conventional methods that rely on limited feedback types, this project aims to develop a unified framework integrating multiple feedback forms, including corrections. The research will focus on theoretical foundations, statistical complexities, and the development of novel learning algorithms. These algorithms will be tested in a simulated scenarios, comparing their performance against traditional reinforcement learning.
AI-GROUNDS
The project, developed in collaboration with the Department of Civil and Environmental Engineering, and with CREA - Centro di Ricerca Ingegneria e Trasformazioni agroalimentari, aims to analyze the relationships between soil characteristics, climatological data and agronomic practices, in order to develop a decision support system based on artificial intelligence, useful for agronomists to identify the most appropriate practices for soil organic carbon management. In this way, the project will enhance the role of agricultural activities in soil carbon storage and create new opportunities for the development of carbon farming.
ELIAS
ELIAS aims at establishing Europe as a leader in Artificial Intelligence (AI) research that drives sustainable innovation and economic development. We will create a Network of Excellence connecting researchers in academia with practitioners in the industry to differentiate Europe as a region where AI research builds towards a sustainable long-term future for our planet, contributes to a cohesive society, and respects individual preferences and rights.
AI4REALNET
The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities.
iBeChange
The IBeChange project aims to design, develop, and test the iBeChange platform. This innovative system is focused on facilitating sustainable behavioral change and emotion management, with a specific emphasis on behaviors scientifically proven to reduce cancer incidence and mortality. IBeChange proposes a new approach that will combine machine learning models with health psychology theories and clinical guidelines to identify effective interventions that can help individuals to reduce the risk of developing cancer.
I3LUNG
I3LUNG is a European project funded under the framework of the H2020 call “Ensuring access to innovative, sustainable and high-quality health care”. Our consortium gathers 16 partners located worldwide characterized by different expertise, with the common goal of providing better assistance and individualize treatment for patients affected by metastatic lung cancer.
FAIR
FAIR is an extended partnership that intends to contribute significantly to the objectives set forth in the Italian Strategic Programme on Artificial Intelligence: advance frontier research in AI: reduce Artificial Intelligence research fragmentation, foster critical mass and inclusion; create human-centered, robust, trustworthy and sustainable AI; foster AI-based innovation and development of AI technology; create, retain and attract AI talent in Italy; ensuring the long-term sustainability of the FAIR Hub.
CLINT
The main objective of CLINT is the development of an AI framework composed of Machine Learning (ML) techniques and algorithms to process big climate datasets for improving Climate Science in the detection, causation and attribution of Extreme Events (EE), including tropical cyclones, heatwaves and warm nights, and extreme droughts, along with compound events and concurrent extremes.
