[Meet our faculty] Roberto Morabito, assistant professor in the Communication Systems department at EURECOM
Q1. What is your academic trajectory?
RM. I hold a Ph.D. in Networking Technology from Aalto University, earned in 2019, and my academic journey has involved exploring various aspects at the intersection of IoT, Edge Computing, and Distributed AI. Before embarking on my doctoral studies, I graduated from Università Mediterranea in Reggio Calabria, Italy. My research interests have shifted over time. I initially focused on the lower layers of the Internet protocol stack during my bachelor’s and master’s studies, but during my PhD, my focus shifted towards networked applications.
I was fortunate to undertake my PhD as part of the Marie Skłodowska-Curie FP7 ITN project ‘METRICS’. It was a great experience, as it gave me the opportunity to collaborate with partners from both industry and academia from day one. As part of the project, I was also hired by Ericsson Research in Finland, where I continued working for eight years, even after completing my PhD. At Ericsson, I worked across the Cloud Systems & Platforms and IoT Technologies & Cyber-Physical Systems teams, gaining a more industry-focused perspective on research. In many ways, my early career spanned both industry and academia, which gave me valuable experience in both sectors.
After completing my PhD, I took on a postdoctoral role at the School of Electrical and Computer Engineering at Princeton University, where my research centered on AI service orchestration in resource-constrained IoT and edge computing environments. I was also responsible for setting up the Edge AI testbed, one of the first of its kind, which was used for several years and several projects, and played a crucial role in advancing our research efforts.
In April 2022, I made the decision to fully return to academia, taking on a Senior Researcher role at the Department of Computer Science, University of Helsinki. I’m particularly grateful for this experience as, along with research, it gave me the opportunity to take full responsibility for teaching several courses for the first time, which was a rewarding experience as I genuinely enjoy teaching. I also fully developed and introduced a new course (‘Networked AI Systems’), contributing to the curriculum development, which was a particularly fulfilling aspect of my role.
In 2024, I joined the Communication Systems Department at EURECOM as an Assistant Professor, where I aim to further advance research and teaching in networked systems and their intersection with AI, IoT, and edge computing technologies. In terms of research, more recently at Helsinki and now at EURECOM, I’ve been exploring the impact of generative AI, particularly Large and Small Language Models, on the distributed networked systems I’ve studied throughout my career.
Q2. What is the expertise you bring to the Communication Systems department?
RM. My expertise lies in the study of distributed networked systems, particularly in understanding how these systems can operate efficiently under computing and communication constraints. I am passionate about finding performance trade-offs that affect the quality of the services being provisioned and introducing components that can improve these trade-offs where possible.
One area that especially fascinates me is studying real-world scenarios characterized by a high degree of heterogeneity — whether in computing and networking capabilities or the variety of applications to be handled within the system. This is particularly relevant in today’s edge computing environments, where such conditions are becoming the norm. I am also interested in the challenges posed by AI’s increasing role in these systems. From the perspective of ‘using AI for better networked systems, a key question I explore is how future AI models can be made interoperable across heterogeneous systems. As AI-driven applications demand more seamless collaboration and optimization between different models and components, emerging AI technologies like Large Language Models are expected to become key parts of next-generation mobile networks.
In terms of practical applications, my work is very hands-on, with prototyping at its core. I have experience in designing and developing solutions that span IoT, edge computing, and AI orchestration, and I have tested these prototypes in real-world scenarios.
I aim to continue advancing research at the intersection of AI and constrained networked systems, pushing the boundaries of resource efficiency and interoperability in these evolving environments. I also plan to reflect this practical experience in my teaching activities, helping students connect theoretical concepts to real-world challenges and solutions.
Q3. What made you choose to come to EURECOM?
RM. Since my bachelor’s and master’s studies, where I worked on areas related to communication systems, I’ve always followed EURECOM’s research dissemination with great interest. The impact it has had in the field, especially in research areas close to mine, has always been impressive. Another standout aspect for me has been EURECOM’s role in the contribution to open-source projects for mobile networks. This focus was truly pioneering, especially given how the open-source community has become central to innovation in mobile networks in recent years. This aligns perfectly with my mindset of turning research ideas into practical, real-world solutions.
During my postdoc at Princeton, I was deeply influenced by my supervisor, Professor Mung Chiang, and the lab’s philosophy, which emphasized bridging theory and practice in networking. I believe that EURECOM and the Communication Systems department embeds this balance between theoretical and practical research, making it an ideal environment for me. The department offers a sweet spot for exploring both ends of the spectrum, which is exactly the kind of atmosphere I want to be part of as I continue developing my work. More generally, as a young faculty member, I was looking for a place where I could be inspired and mentored by experienced and talented researchers and educators and I felt that EURECOM can provide me this kind of environments.
Additionally, EURECOM’s close ties with industry partners resonate with my own background, having worked at the intersection of research and industry for several years. This connection provides the opportunity to develop solutions that are both academically rigorous and highly relevant to real-world applications. Finally, the multicultural environment at EURECOM also brings diverse perspectives, enriching both research and teaching, and I am excited to be part of this community.
Q4. What are your future goals and if you had to sketch a five-year plan for your research goals, what would that be?
RM. While I expect the backbone of my research to continue building on my recent efforts, the field is evolving rapidly. One significant shift is the growing impact of Generative AI and Large Language Models, which is already reshaping many aspects of distributed networked systems. I aim to contribute to this transformation by exploring how these models can potentially redefine key fundamentals of network design.
Beyond my current activities in this area, I envision many exciting possibilities. For example, I see these models potentially becoming an integral part of network protocols, helping us not only to better understand and optimize them but also to fundamentally rethink how communication is structured. In this respect, I believe they hold the potential for being key enablers for concepts like semantic communication, which has moved beyond theory but remains in its early stages of practical implementation. This connects back to the idea of bridging theory and practice, which I will try make central to my approach. I hope to make an impact by bringing these emerging concepts closer to real-world application, while still focusing on the constraints that have defined my research — such as heterogeneity, interoperability, and ensuring optimal performance in diverse, resource-constrained edge environments. If I were to summarize my research ambition, it would be to define the key fundamentals of ‘next-generation edge intelligence.’
From a teaching perspective, I plan to introduce new courses that reflect the interdisciplinary nature of AI, IoT, and edge computing. These courses will cover emerging technologies but will also integrate hands-on projects, potentially in collaboration with industry. I am particularly keen to foster a culture of open and reproducible research, ensuring students are equipped with the skills needed for future challenges in academia and industry.
