Projects and research

Description: This study aims to develop explainable machine-learning and deep-learning models to identify ECG-derived signatures associated with LGE-positive cardiomyopathies, currently detectable only through contrast-enhanced cardiac magnetic resonance imaging. If successful, the project would provide a low-cost, non-invasive proxy based on routine ECG recordings to support early risk stratification and guide referral to specialised, more expensive and invasive diagnostic pathways only for patients with suspicious ECG profiles. The study is carried out in collaboration with ASUGI–Cattinara.

Reference publications: coming soon.

I recently joined the Working Group 4 "Mathematical & Computational Modelling", lead by Prof. Claudio Angione (Teesside University, UK).

Description: This COST Action aims to significantly facilitate the translation of fundamental scientific discoveries into better clinical treatment and management of patients suffering from brain cancer. This aim will be pursued through the following main objectives: 1) to build a unique pan-European and multidisciplinary network focusing on brain cancer by combining state-of-the-art knowledge and innovative techniques; 2) to promote education and training in the areas of advanced neuroscience, neuroimaging, genetics and molecular biology, big data and computational techniques for the accurate early diagnosis, prognosis, patient stratification and treatment of patients with different types of brain cancer; and 3) to build an integrated pan-European brain cancer database and biobank platform for the benefit of the research and clinical community.

Funding: COST.

Period: 30/10/2023 -  29/10/2027

Website: see here.

The management of greenhouses represents a complex and dynamic task that requires high costs in terms of energy consumption and professional skills, and typically highly impacts on the environment. The sustainability of new generations of greenhouses calls for new integrated monitoring systems, which can continuously and quantitatively monitor different parameters of the plants and their environment, using smart algorithms to promptly identify health issues and adaptively actuate actions to optimize the plants' growth and prevent epidemics. This project aims to prototype a system to manage smart and autonomous greenhouses where a number of NIRS-based sensors, produced by Seletech Engineering srl, are deployed to precisely and continuously retrieve information from the greenhouse. The system will take advantage of AI-based models and cloud technology, and it is expected to bring greenhouses closer to sustainability, with a significant reduction of the use of fertilizers and pesticides.

Partners: University of Milano-Bicocca (Dept. Informatics, Systems and Communication), University of Padova (Dept. Information Engineering), Seletech Engineering srl.

Funding: Italian Operative National Program (PON) 2014-2020, "Research and Innovation - Green". 

Period: 24/01/2022 -  30/09/2024

Reference publications (see Publications section): [J9, J10, J11, C25, C27, C29, C30, C32, C33, C36🏆].