Spotlight Seminars on AI – Spring 2026

The Italian Association for Artificial Intelligence is pleased to announce the Spring 2026 program of the Spotlight Seminars on AI.

The aim of the seminars is to illustrate, explore and discuss current scientific challenges, trends, and possibilities in all branches of our articulated research field. The seminars will be held virtually (https://www.youtube.com/c/AIxIAit), on a monthly basis, by leading Italian researchers as well as by top international scientists. The seminars are mainly aimed at a broad audience interested in AI research, and they are also included in the Italian PhD programme in Artificial Intelligence; indeed, AIxIA warmly encourages the attendance of young scientists and PhD students.

The “Spring 2026” edition features 3 seminars:

• Prof. Fabrizio Silvestri, University “La Sapienza” Roma
  • April 30 – 5:00 PM (CEST)
  • Title: Learning with Structured Consistency: Sheaf Neural Networks from Theory to Applications
  • Abstract: Modern machine learning on graphs is largely built on the assumption that neighboring nodes should be similar, an inductive bias that breaks down in the presence of heterogeneity, multi-modality, and relational ambiguity. Sheaf Neural Networks offer a principled alternative by replacing this notion of similarity with structured consistency: information is no longer directly compared across nodes, but aligned through learnable transformations defined on edges and, more generally, on higher-order relations.In this talk, I introduce the sheaf-theoretic perspective as a natural and flexible framework for learning over complex relational data, spanning both graphs and hypergraphs. I will motivate the approach through concrete failure modes of standard Graph Neural Networks, and show how sheaves provide a unified solution to modeling directionality, heterophily, and context-dependent interactions, while naturally extending to higher-order structures beyond pairwise connections. I will then present a sequence of theoretical results that characterize the expressivity, stability, and spectral properties of Sheaf Neural Networks, highlighting their role as a strict generalization of classical architectures.Finally, I will illustrate how these ideas translate into practice through applications in recommender systems, where modeling structured inconsistency leads to significant empirical improvements. I conclude by discussing open challenges and future directions, including scalable learning of sheaf structures on hypergraphs and connections to broader geometric deep learning paradigms.
  • Bio: Fabrizio Silvestri is a Full Professor at Sapienza, University of Rome in Italy. Formerly a Research Scientist at Facebook AI in London. His interests are in AI applied to integrity-related problems and Natural Language Processing. In the past, he has worked on web search research, and in particular, his specialization is building systems to interpret better queries from search users. Before Facebook, Fabrizio was a principal scientist at Yahoo, where he had worked on sponsored search and native ads within the Gemini project. Fabrizio holds a Ph.D. in Computer Science from the University of Pisa, Italy, where he studied problems related to Web Information Retrieval with a particular focus on Efficiency-related problems like Caching, Collection Partitioning, and Distributed IR in general.

• Prof. Leslie Pack Kaelbling– MIT
  • May 29 – 3:00 PM (CEST) – in collaboration with FBK academy
  • Title: The Role of Rationality in Modern Robotics
  • Abstract: The classical approach to AI designed systems that were rational at run-time: they had explicit representations of beliefs, goals, and plans and ran inference algorithms, online, to select actions. The rational approach was criticized (by the behaviorists) and modified (by the probabilists), but persisted in some form. More recently, relatively unstructured data-driven end-to-end approaches have demonstrated great success in a wide variety of domains and began to seem like a plausible route to general-purpose intelligent robots. However, most recently, we have begun to see the limits of pure behavior learning, and many practitioners are re-integrating forms of search and explicit reasoning into their approaches.
  • I will revisit the rational-agent approach to the design of intelligent robots, from the perspectives of engineering effort, computational efficiency, cognitive modeling and understandability. I will present some current research focused on understanding the roles of learning in runtime-rational agents with the ultimate aim of constructing general-purpose human-level intelligent robots.
  • Bio: Leslie is a Professor at MIT. She has an undergraduate degree in Philosophy and a PhD in Computer Science from Stanford, and was previously on the faculty at Brown University. She was the founding editor-in-chief of the Journal of Machine Learning Research. Her research agenda is to make intelligent robots using methods including learning, planning, and reasoning about uncertainty.  She was doing agentic AI way before it was cool.

• Prof. Diego Calvanese, University “La Sapienza” Roma
  • June 24 – 5:00 PM (CEST)
  • Title: TBA
  • Abstract: TBA
  • Bio: Diego Calvanese received a PhD from Sapienza University of Rome in 1996. He is the author of more than 350 refereed publications, including ones in the most prestigious international journals and conferences in Artificial Intelligence and Databases, with more than 33000 citations and an h-index of 72, according to Google Scholar. He has served in over 150 program committee roles for international events, and is/has an associate editor of Artificial Intelligence and of JAIR. In 2012-2013, he was a visiting researcher at TU Vienna as Pauli Fellow of the “Wolfgang Pauli Institute”. He has been the program chair of PODS 2015 and KR 2020 and the general chair of ESSLLI 2016. He was nominated as an EurAI Fellow in 2015 and ACM Fellow in 2019.