The SmartData@PoliTO center focuses on Big Data technologies, Data Science and Machine Learning approaches. We blend interdisciplinary people and competences from different domains to provide cross-domain solutions to the widest spectrum of knowledge discovery challenges, by leveraging advanced expertise in data science, from data management, to data modeling, analytics, and engineering. We are a well-recognized center where experts in methodologies and domain experts from various disciplines work in a single space, facing both theoretical problems and helping companies toward applications.
Presenter: Andrea SordelloThursday, June 19th, 2025, 5:00 PMLocation: Sala Grande Covivio, Corso Ferrucci 112 ABSTRACT Andrea Sordello is a PhD student in Computer and Control Engineering at Politecnico di Torino, within the SmartData@PoliTO research center. His research focuses on network traffic analysis and the development of distributed platforms for cybersecurity, as part of the SERICS […]
Presenter: Kai HuangThursday, June 5th, 2025, 5:00 PMLocation: Sala Grande Covivio, Corso Ferrucci 112 ABSTRACT The rapid evolution of cyber threats and the increasing sophistication of cyberattacks have made digital forensics a cornerstone of modern cybersecurity. In this context, Large Language Models (LLMs) present a compelling opportunity to augment cybersecurity forensics. To investigate the capabilities […]
On May 20th 2025 at 16:00, in Luigi Ciminiera meeting room, Politecnico di Torino, the SmartData@Polito Center will host the companies associated with the Digital Technologies Group of Unione Industriali Torino, a Group that includes about 170 companies, with over 12 thousand collaborators, that enable and support the digital transition of the economy: from TLC […]
Presenter: Pierrick LeroyThursday, May 8th, 2025, 5:00 PMLocation: Sala Grande Covivio, Corso Ferrucci 112 ABSTRACT Face Recognition (FR) tasks have significantly progressed with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into high-dimensional feature spaces. During training, these contrastive losses focus exclusively on identity information as labels. However, […]
