The Laboratory of Biomedical and Health Informatics is a network of research groups and individual researchers of the Department of Computer Science that aims at favoring the collaboration between experts in different Computer Science disciplines and methodologies  in research projects with applications in Biology, Medicine and Health.

Modern high throughput sequencing technologies make a huge amount of genomic sequences available to the broad scientific community. This calls for sophisticated algorithms and data structures for their in silico investigation. We design and realize efficient algorithms for features discovery of large collections of biological sequences, trees, and graphs/networks.

Research group focused on modelling, simulation and verification of biological systems. Expertise in modelling and analysis of cell pathways and gene regulatory networks with systems biology approaches grounded on formal methods of computer science.

Computing technologies to enhance biomedical research and to improve health and quality of life. AI, bioinformatics and computational biology with applications in medicine, pharmacology, biotechnology and nutrition science.

The group has experience in Artificial Intelligence methodologies, ranging from Computational Intelligence to Machine Learning approaches, with a scientific international leadership in topics for learning in structured domains (sequences, trees and graphs/networks).

Research group on Cyber-Physical Systems for e-Health and Ambient Assisted Living

The Data Science for Sports Analytics group has a multidisciplinary background, i.e. machine learning technique, big data analysis and sports science. The main topics of this group involve the development of models in sports and in particular in soccer that permits to deeply assess the sport performance both in physical demands and technical-tactical performances.

Home Care Services are all services (medical, paramedical and social services) which are delivered at the premises of the patient. We develop mathematical models and optimization algorithms to solve challenging problems arising in Home Care. They address complex timing constraints, resource management issues and uncertainty aspects, such as unexpected resource shortage or shock of patient demand.

The research group is involved in projects in collaboration with agricultural researchers in which IoT technologies, mobile applications and cloud are applied, in synergy with computational modeling and machine learning methods. The aim is to develop decision support systems for crop monitoring, and for the optimization of production, irrigation and treatments.