Marco Savi is an Associate Professor at the University of Milano-Bicocca (Milano, Italy). He received his BSc (2010) and MSc (2012, summa cum laude) in Telecommunications Engineering (Networking area) both from Politecnico di Milano (Milano, Italy). His master thesis investigated users’ privacy concerns in a Smart Grid scenario. In June 2016 he pursued a PhD degree in Information Technology at Politecnico di Milano. During his PhD studies he worked on Content Delivery and on the emerging Network Function Virtualization (NFV) paradigm. From March 2016 to March 2020 he worked with Fondazione Bruno Kessler (Trento, Italy) initially as Postdoc Researcher, being promoted to Expert Researcher in 2018. He was involved on several research topics including Fog Computing, Network Monitoring, SDN Multi-layer Networks and NFV. In April 2020 He joined the Department of Informatics, Systems and Communication (DISCo) of University of Milano-Bicocca as Non-Tenured Assistant Professor (RTD-A), becoming Tenure-Track Assistant Professor (RTD-B) in October 2022 and Associate Professor in October 2025. He is currently working on various topics related to computer networking, including Serverless Computing applied to NFV, Programmable Data Planes for Network Monitoring and Machine Learning applied to Network Security. He also carries on teaching activities. He has published more than 70 peer-reviewed papers in reputed international journals and conferences.

Fabio Sartori is an Associate Professor at the Department of Informatics, Systems and Communication of the University of Milano-Bicocca. He teaches “Algorithms and Programming” and “Laboratory of Mathematics and Informatics” courses at the Department of Mathematics and its Applications of the University of Milano-Bicocca. His research activity mainly concerns knowledge-based systems and knowledge management. In these contexts, he has taken care of both methodological and computational aspects in the development of decision support systems, exploiting the Case-Based Reasoning (CBR) paradigm. Recently, he has applied the CBR paradigm in Artificial Intelligence for Society and Economy research field, taking part in the many research projects, funded from both public and private institutions, and studying how the CBR problem solving method can be used in forecasting and, possibly, avoiding, enterprise bankruptcy. He is involved, as Programme Committee Member, in the organization of many International Conferences in the Knowledge Management and related research areas: in particular, he is one of the founders of the Metadata and Semantics Research Conference Series (MTSR) which aims to bring together scholars and practitioners that share a common interest in the interdisciplinary field of metadata, semantics, linked data and ontologies. He is Associate Editor of Program, an inter-disciplinary journal covering Computing and Information Science and also the Social Sciences in general and any other discipline that is concerned with digital data. Moreover, he serves as reviewer for many journals related to different topics of Computer Science. He belongs to the Committee of International Experts of the doctoral programme “Knowledge and Information Engineering” of the Computer Science Department at the University of Alcalà (Spain).

Emanuele Petriglia is a PhD student in Computer Science at the University of Milano-Bicocca (Milan, Italy). He received his BSc in Computer Science from the Sapienza University of Rome (Rome, Italy) in 2021 with a grade of 110/110 cum laude. Then he received the MSc in Computer Science from the University of Milano-Bicocca in 2024 with a grade of 110/110 cum laude. His Master’s thesis project deals with the use of multi-agent reinforcement learning for workload distribution in FaaS edge computing systems. From 2023, he is working as an adjunct professor for the Distributed Systems course of the BSc Computer Science degree at the University of Milano-Bicocca, and from 2024 also as a laboratory tutor for the same course. His research interests are in the area of edge computing combined with reinforcement learning.

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