Research is our passion

We work on extremely innovative projects across different fields, such as manufacturing, agriculture, aerospace engineering, healthcare, telecommunications, and transportation.

We tackle new technological challenges thanks to our talented and skilled team, and with the support of world-class partners.

AI-Magister

AI Magister is one of the EDIH (European Digital Innovation Hubs), funded by the Ministry of Enterprises and Made in Italy (MIMIT) with a focus on Artificial Intelligence.

View Project

The DAFIOLog initiative is born out of the necessity to establish a robust data aggregation and governance framework capable of supporting the diverse range of stakeholders, both public and private, operating within and around the Port.

View Project

SInISA

The SInISA project is inspired by the pressing need to address the challenges posed by the aging population, particularly in the realms of health, social welfare, and economics.

View Project

BAI4LOG

The project is driven by the substantial changes occurring in the transport and logistics sector, encompassing both Business Transformation and Digital Transformation.

View Project

Distrimuse

The project aims at enhancing sensing capabilities to perceive human presence, behavior, and health within collaborative environments through multi-sensor systems.

View Project

Resilient Trust

The Resilient Trust project aims to address the challenges and opportunities presented by the Internet of Things (IoT) and its evolution to IoT5.0, which is characterized by AI-assisted devices.

View Project

SMART STOP

The SMART STOP project studies the design and prototyping of a high-tech platform aimed at monitoring and collecting data in the field of smart mobility.

View Project

Fractal

The objective of the FRACTAL project is to introduce a novel approach to reliable edge computing, by creating a Cognitive node under industry standards.

View Project

NextPerception

The goal of this project is to develop next generation smart perception sensors and enhance the distributed intelligence paradigm to build versatile, secure, reliable, and proactive human monitoring solutions for the health, wellbeing, and automotive domains.

View Project

Valu3s

VALU3S aims to design, implement and evaluate state-of-the-art V&V methods and tools in order to reduce the time and cost needed to verify and validate automated systems with respect to safety, cybersecurity and privacy (SCP) requirements.

View Project

Pickup

The PICK-UP project aims at implementing innovative methods and tools for energy and environmental management and consumption reduction in heterogeneous urban districts.

View Project

Liguria 4P Health

Development of an innovative solution of personal/mobile healthcare based on the semantic management of clinical data obtained from wearable/environmental created through predictive algorithms in order to create efficient recruiting, care and rehabilitation plans.

View Project

SafeCop

SafeCOP addresses operating environments with security constraints such as Cooperating CyberPhysical Systems (CO-CPS) characterised by a prevailing use of wireless communication with multiple stakeholders and open and unpredictable operating environments.

View Project

P3C

P3C aimed at creating a virtuous circle involving (1) predictive medicine, (2) medical records (Italian Fascicolo Sanitario Elettronico, FSE), (3) diagnostic appropriateness, and (4) personalized therapy.

View Project

publications

Our scientific articles

Ferrari Enrico and Muselli Marco, “Efficient constructive techniques for training switching neural networks.” Constructive Neural Networks. Springer, Berlin, Heidelberg, 2009. 25-48.
Muselli Marco and Ferrari Enrico, “Coupling Logical Analysis of Data and Shadow Clustering for partially defined positive Boolean function reconstruction.” IEEE Transactions on Knowledge and Data Engineering 23.1 (2011): 37-50.
Parodi Stefano et al., “Differential diagnosis of pleural mesothelioma using Logic Learning Machine.” BMC bioinformatics 16.9 (2015): S3.
Agosta Giovanni et al., “V2I Cooperation for traffic management with SafeCop.” Digital System Design (DSD), 2016 Euromicro Conference on. IEEE, 2016.
Agneessens Alessio et al., “Safe cooperative CPS: A V2I traffic management scenario in the SafeCOP project.” Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), 2016 International Conference on. IEEE, 2016.
Parodi Stefano et al., “Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables.” Health informatics journal (2016): 1460458216655188.
Parodi Stefano, et al., “Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine.” Journal of gambling studies 33.4 (2017): 1121-1137.
Mongelli Maurizio et al., “Performance validation of vehicle platooning via intelligible analytics.” IET Research Journals, 2018: 1–8
Fermi Alessandro, et al., “Identification of safety regions in vehicle platooning via machine learning.” 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS). IEEE, 2018.
Banerjee Imon et al., “Feature-based Characterisation of Patient-specific 3D Anatomical Models”, Smart Tools and Applications in Graphics – Eurographics Italian Chapter Conference, 2019.
Verda Damiano et al., Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods. BMC Bioinformatics 20, 390 (2019).
Mongelli Maurizio et al., “Accelerating PRISM validation of vehicle platooning through machine learning,” in 2019 4th International Conference on System Reliability and Safety (ICSRS). IEEE, 2019, pp. 452–456.
Mongelli Maurizio et al., “Achieving zero collision probability in vehicle platooning under cyber-attacks via machine learning,” in 2019 4th International Conference on System Reliability and Safety (ICSRS). IEEE, 2019, pp. 41–45.
Mongelli Maurizio et al., “Performance Validation of Vehicle Platooning via Intelligible Analytics”, IET Cyber-Physical Systems: Theory & Applications, 19 Oct. 2018.
Aiello Maurizio et al., “Unsupervised learning and rule extraction for Domain Name Server tunneling detection” Internet Technology Letters 2019; 2:e85. https://doi.org/10.1002/itl2.85.
Barbosa Raul et al., “The VALU3S ECSEL Project: Verification and Validation of Automated Systems Safety and Security,” 2020 23rd Euromicro Conference on Digital System Design (DSD), 2020, pp. 352-359, doi: 10.1109/DSD51259.2020.00064.
Lojo Aizea et al., “The ECSEL FRACTAL Project: A Cognitive Fractal and Secure edge based on a unique Open-Safe-Reliable-Low Power Hardware Platform” 2020 23rd Euromicro Conference on Digital System Design (DSD), 2020, pp. 393-400, doi: 10.1109/DSD51259.2020.00069.
Gerussi Alessio et al., “Machine learning in primary biliary cholangitis: A novel approach for risk stratification”, Liver Int. 2022; 00: 1– 13. doi:10.1111/liv.15141.