• M.O.D.A.L.

  • Mathematical mOdelling and Data AnaLysis

    Department of Mathematics and Applications "R. Caccioppoli"

  • Mathematical mOdelling and Data AnaLysis

    Deep Learning methodologies for Medicine and Healthcare

  • Physics-Informed Neural Networks

  • Mathematical mOdelling and Data AnaLysis

    Machine Learning in Biochemistry

  • Mathematical mOdelling and Data AnaLysis

    Deep Learning for Market Neutral Portfolio

  • Mathematical mOdelling and Data AnaLysis

    Artificial Intelligence for fighting COVID-19

  • Mathematical mOdelling and Data AnaLysis

    IoT and Deep Learning in Cultural Heritage

The M.O.D.A.L. research group at the University Naples Federico II conducts research and teaches a wide range of AI related topics, often cross-disciplinary. We collaborate internationally and locally with academics, businesses and public stakeholders, working at the frontiers of knowledge on solving real-world problems. The M.O.D.A.L. research group is dynamic and community-oriented, providing many research collaboration and innovation opportunities. M.O.D.A.L. is committed to advancing knowledge and fostering learning in an atmosphere of discovery and creativity. People-oriented values such as transparency, trust, creativity and autonomy are central. Head and Scientific Coordinator: Prof. Francesco Piccialli

Research Activities

M.O.D.A.L. research interests fall within the following main areas:

Machine and Deep Learning

Machine learning means computers learning from data using algorithms to perform a task without being explicitly programmed. Deep learning uses a complex structure of algorithms modeled on the human brain. This enables the processing of unstructured data such as documents, images and text.

Scientific Machine Learning

Nowadays, combining physics law and domain knowledge into Machine Learning models can be considered a new frontier; the goal is to provide some “informative priors” such as theoretical constraints on top of observational ones. Therefore, Physics-Informed Machine Learning aims to introduce a novel paradigm to improve the performance of the learning algorithms.

Data Science

Data science is a field of applied mathematics and statistics that provides useful information based on large amounts of complex data or big data.
Data science, or data-driven science, combines aspects of different fields with the aid of computation to interpret reams of data for decision-making purposes.

Real-World Applications

Healthcare, Precision Medicine, Industry 4.0, Smart City, Smart Mobility, Agritech, Biochemistry, Cultural Heritage, Energy Efficiency, Geosciences, Seismology.

Last Projects

Deep-Learning-aided GPC-IR fingerprinting of complex polyolefin mixtures

By combining deep learning with GPC-IR fingerprinting, we hope to unlock new possibilities for analyzing and characterizing these materials, and to provide valuable insights into their properties and behavior. This research is being carried out in partnership with the Department of Chemical Sciences of the University of Naples Federico II.

4I: mixed reality, machine learning, gamification and educational for Industry

Il progetto 4I, diretto al miglioramento dei processi esistenti, intende proporre alle industrie un nuovo modello di condivisione della conoscenza, di supervisione e manutenzione dei processi e delle attrezzature e di formazione del personale e QA, Quality Assurance, sfruttando le nuove tecnologie di mixed reality, il machine learning, l’educational e la gamification.

ELIXIR x NextGenerationIT

This project aims to strengthen the Italian infrastructure for omics data and bioinformatics through collaboration between ELIXIR, an international organization for managing and sharing life science data, and NextGenerationIT, an Italian IT company specializing in genomics and bioinformatics.

Last News

Last News @ M.O.D.A.L.

Blockchain-based Secure Internet of Medical Things Framework for Stress Detection

We are thrilled to announce that our paper “A Blockchain-based Secure Internet of Medical Things Framework for Stress Detection” has been pu

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Highly Cited Paper in 2022

We're thrilled to share that our article on "From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0" has been recog

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Welcome to a new member of MODAL!

We are excited to welcome Marzia Canzaniello as new researcher to our MODAL laboratory! With her expertise and skills, we're looking forward to

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