{"id":56,"date":"2019-03-26T08:07:46","date_gmt":"2019-03-26T07:07:46","guid":{"rendered":"http:\/\/www.labdma.unina.it\/?page_id=56"},"modified":"2026-05-22T12:47:56","modified_gmt":"2026-05-22T10:47:56","slug":"groups","status":"publish","type":"page","link":"https:\/\/www.labdma.unina.it\/index.php\/groups\/","title":{"rendered":"The Group"},"content":{"rendered":"\n\n\n

M.O.D.A.L. – M<\/strong>athematical mO<\/strong>delling and D<\/strong>ata A<\/strong>naL<\/strong>ysis research group<\/p>\n\n

\nIn recent years, Deep Learning and Federated Learning have become central paradigms in the evolution of Artificial Intelligence, reshaping the way complex data are analyzed, shared, and transformed into actionable knowledge. Our research group focuses on the development of advanced Machine Learning and Deep Learning methodologies, with particular attention to privacy-preserving, distributed, and collaborative learning approaches enabled by Federated Learning.\nBuilding on these foundations, the group explores the latest developments in Generative AI to address complex challenges across different domains, supporting the automation of sophisticated processes, from predictive analytics to real-time decision-making. These methods allow patterns, structures, and knowledge to emerge naturally from diverse, heterogeneous, and large-scale datasets.\nDigital Twin technologies play an integral role in our research, enabling the creation of virtual replicas of physical systems. This approach supports real-time simulations, predictive insights, and optimized decision-making in areas such as smart cities, energy management, autonomous systems, and industrial processes. When combined with Generative AI, digital twins allow the simulation and anticipation of scenarios with greater precision, providing a deeper understanding of complex phenomena.\nOur work also investigates Scientific Machine Learning (SciML), combining scientific principles with modern AI techniques to improve the interpretability, reliability, and accuracy of computational models. This integration helps bridge the gap between data-driven approaches and domain-specific knowledge, enabling a more robust understanding of intricate physical and engineered systems.\nThe group also has strong scientific expertise in High-Performance Computing (HPC), with particular attention to parallel algorithms, scalable numerical methods, distributed computing, and the efficient execution of data-intensive and simulation-driven workloads. These competences support the development of robust AI, Digital Twin, and SciML solutions for complex scientific and industrial problems.\nOverall, our expertise in mathematics, statistics, computer science, Federated Learning, Deep Learning, Scientific Machine Learning, Digital Twins, and High-Performance Computing drives the development of innovative AI solutions designed to address the challenges of today and tomorrow.\n\n<\/p>\n\n

People<\/p>\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \"Francesco\n\t\n \n <\/td>\n\n \n \"Salvatore\n\t\n \n <\/td>\n\n \n \u201cMarco\n\n \n <\/td>\n\n \n \"Valeria\n\n \n <\/td>\n\n\n <\/tr>\n

Francesco Piccialli – Founder, Head, and Scientific Coordinator of the M.O.D.A.L. group, Full Professor of Computer Science<\/p> <\/td>\n \n

Salvatore Cuomo – Co-Founder of the M.O.D.A.L. group, head and coordinator of the Scientific Machine Learning team, Full Professor of Numerical Analysis<\/p> <\/td>\n\t\n

Marco Lapegna, member of the M.O.D.A.L. group, coordinator of the High-Performance Computing activities, Associate Professor of Computer Science <\/p><\/td>\t\n

Valeria Mele, member of the M.O.D.A.L. group, Associate Professor of Computer Science <\/p><\/td>\n \n <\/tr>\n \n \n\n

\"Fabio<\/td>\n \"Daniela<\/td>\n \"Stefano<\/td>\n \"Marzia<\/td>\n \n <\/tr>\n
Fabio Giampaolo<\/td>\n Daniela Annunziata<\/td>\n Stefano Izzo<\/td>\n Marzia Canzaniello<\/td>\n\n <\/tr>\n
Ph.D., Assistant Professor (RTT – Tenure Track) of Computer Science<\/td>\n Ph.D., Research Fellow<\/td>\n Ph.D., Research Fellow<\/td>\n Ph.D., Research Fellow<\/td>\n\n <\/tr>\n
Scholar Link<\/a><\/td>\n Scholar Link<\/a><\/td>\n Scholar Link<\/a><\/td>\n Scholar Link<\/a><\/td>\n\n <\/tr>\n\n\n
\"Sara<\/td>\n \"Valentina<\/td>\n \"Anna<\/td>\n \"Lingyu<\/td>\n\n <\/tr>\n
Sara Amitrano<\/td>\n Valentina De Angelis<\/td>\n Anna Borrelli<\/td>\n Lingyu Qiu<\/td>\n\n \n \n <\/tr>\n \n
Ph.D. student<\/td>\n Ph.D. student<\/td>\n Ph.D. student<\/td>\n Ph.D. student<\/td>\n\n <\/tr>\n
Scholar Link<\/a><\/td>\n <\/a><\/td>\n <\/a><\/td>\n Scholar Link<\/a><\/td>\n\n\n <\/tr>\n \n
\"SM<\/td>\n\t\"\"<\/td>\n\t\"\"<\/td>\n\t\"\"<\/td>\n\n <\/tr>\n
SM Asiful Huda<\/td>\n Sundas Sarwar<\/td>\n Makhmoor Fiza Murk<\/td>\n <\/td>\n\n <\/tr>\n
Ph.D. student<\/td>\n Ph.D. student<\/td>\n Ph.D. student<\/td>\n <\/td>\n \n <\/tr>\n\t
Scholar Link<\/a><\/td>\n Scholar Link<\/a><\/td>\n Scholar Link<\/a><\/td>\n <\/a><\/td>\n\n\n <\/tr>\n\n \n \n<\/table>\n
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Past Ph.D. students and Research Fellows<\/p>\n\n