{"id":696,"date":"2026-08-21T13:02:27","date_gmt":"2026-08-21T13:02:27","guid":{"rendered":"https:\/\/nof.dnac.org\/2026\/?page_id=696"},"modified":"2026-08-21T13:10:35","modified_gmt":"2026-08-21T13:10:35","slug":"panel","status":"publish","type":"page","link":"https:\/\/nof.dnac.org\/2026\/panel\/","title":{"rendered":"Panel"},"content":{"rendered":"

Panel<\/strong><\/span><\/h2>\n

Title: Networks in the AI Era: Data as a Goldmine for Research and Innovation<\/em><\/strong><\/p>\n

Abstract:<\/strong> Artificial intelligence is now permeating the landscape of Internet networks and services, whether it is for innovative services targeting customers, fast decision-making within the control plane, or monitoring for KPI and security. Algorithms derived from machine learning, deep learning, neural networks, and natural language processing agents are already integrated or on the way to becoming central to the hardware and software infrastructures of the future networks. At the heart of this evolution lies the data necessary for these new algorithms to operate, but producing it in sufficient quantity and quality, and making it available to the stakeholders involved in designing these new components and services for the networks of the future, remains a significant challenge. In this panel of experts, we propose to identify the obstacles and challenges related to data and datasets from different perspectives. We will first address the technical challenges of producing the data needed to train the models used in networks, from capturing real-world data to generating synthetic ones. What quality, variety, and quantity of data are needed to accurately reflect the extremely diverse situations our networks face? Secondly, we will address the dissemination and use of data. What are the challenges of anonymization for privacy and the sensitivity of disseminated data for algorithms designed to assist, among other things, security operators? What framework and scope of dissemination are acceptable to provide access to data, particularly for public and private actors in research and innovation? Finally, like a gold mine, the economic stakes surrounding data will be explored due to the difficulty of producing and disseminating it. What profits and which actors are behind data and datasets for innovative networks and services? What role does public dissemination and free usage play in a context where data is so scarce and crucial? This panel will provide a view on these challenges from four senior experts regarding various academic, industrial, and governmental sectors, allowing the preceding questions to be addressed from complementary and sometimes contradictory perspectives.<\/p>\n

\n

Panelists:<\/strong><\/span><\/h4>\n
\"Jovanka

Jovanka Adzic<\/b>
(University of Belgarde, Serbia)<\/p><\/div><\/div>\n

Bio: Jovanka<\/em><\/span>\u00a0Adzic<\/em><\/b> obtained Political Science degree in International Law (University of Belgarde, Serbia), Computer Science degree in Data Management (University of Turin, Italy), Master in Informatics and Telecommunications (Polytechnic of Turin, Italy) and Master in European Law and Policies (UniNettuno, Rome, Italy). She joined research and innovation department at Telecom Italia as project and technical leader where she contributed to and coordinated many innovative internal projects such as ML-based solutions for fraud detection and management. Moreover, she contributed also to European collaborative projects, and particularly to those addressing AI\/ML across connect-compute networks, such as AI@EDGE project. Her recent research interests lie in the exploration of the AI\/ML for mobile networks and cloud-edge computing infrastructures, with particular interest in AI-enabled network management, federated learning and AI Governance.<\/p>\n

\n
\"Kohei

Kohei Shiomoto<\/b>
(Tokyo City University, Japan)<\/p><\/div><\/div>\n

Bio: Kohei\u00a0Shiomoto<\/em> <\/span><\/b>is a Professor at Tokyo City University, Tokyo, Japan. Before joining the university in 2017, he was with NTT Laboratories, where he conducted and led research on network architecture, traffic engineering and management, and network operations for the Internet, mobile networks, and cloud computing infrastructures. His research focuses on the intersection of artificial intelligence and computer networks, with particular interests in AI-enabled network management, trustworthy machine learning, federated learning, and secure communication infrastructures.<\/span><\/p>\n

\n
\"Gregory

Gregory Blanc<\/b>
(Nara Institute of Science and Technology (NAIST), Japan)<\/p><\/div><\/div>\n

Bio: Gregory Blanc <\/em><\/span><\/b>obtained a Ph.D degree in cybersecurity from Nara Institute of Science and Technology (NAIST, Japan). He then joined SAMOVAR laboratory at T\u00e9l\u00e9com SudParis as a postdoctoral fellow where he contributed to the proposal and coordination of European collaborative projects such as NECOMA (joint Europe-Japan project). Since 2015, he is an associate professor in networks and cybersecurity at T\u00e9l\u00e9com SudParis and coordinates the education program in systems and networks security. His recent research interests lie in the exploration of machine learning (ML) and network programmability to the benefit of the autonomic security loop in order to improve the resilience of networks of the future, incl. monitoring, detection, countermeasure selection, policy security orchestration. He is also active on topics dealing with the critical assessment of such approaches including the evaluation of ML-based systems reliability, security and robustness, and of the quality of the related datasets.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

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