The Shapes of Knowledge: Topological and Geometric Methods to Learn on Complex Networks - Seminario

Dettagli

Venerdì 23 Gennaio 2026 alle ore 11.30-13.00, nell'Aula Magna del Polo di Ingegneria,  il Dott. Claudio Battiloro (ex-studente di Laurea triennale e attualmente Post Doc alla Harvard University) terrà un seminario dal titolo "Artificial Aerodynamics".

Per prenotare il posto in Aula Magna e facilitare l'organizzazione siete invitati a registrarvi tramite Eventbrite a questo link:
https://www.eventbrite.it/e/talk-of-doct-claudio-battiloro-the-shapes-of-knowledge-tickets-1981109130740

Talk Abstract

Current frontiers in machine learning, data science, and, more broadly, artificial intelligence reveal the limits of purely predictive approaches and motivate a shift toward decentralized, scalable, and causal systems. Such systems require processing and learning on increasingly complex networks. A promising heterogeneous toolbox, loosely grouped under the name of Topological Deep Learning (TDL), aims to design deep architectures that integrate ideas from algebraic topology, non-Euclidean geometry, and category theory to address this complexity. In Dr. Battiloro’s approach to TDL, the basic units of a network are cells, which generalize graph nodes. A cell may represent, for example, a single agent or a group of agents in an agentic AI system, a neuron or a brain region in a neural circuit, or a sensor or sensor type in an environmental monitoring network. Cells can be organized hierarchically and exhibit rich intra-and inter-cell interaction patterns. In this seminar, Dr. Battiloro will (1) introduce and discuss TDL’s current landscape and explain why developing a modern, coherent language for it matters broadly, (2) argue that this language should be grounded in the theory of poset sheaves, (3) briefly highlight goals TDL has already achieved—such as inferring higher-order, hierarchical goal-driven interactions in data or jointly modeling and relating subjective causal structures across cells—and (4) outline an ambitious sheaf-centric research pathway for TDL.

Biography

Dr. Battiloro is a postdoctoral fellow at the Harvard T.H. Chan School of Public Health in the NSAPH group supervised by Prof. Francesca Dominici. He is part of the Harvard Data Science Initiative. He is a former Visiting Associate at the University of Pennsylvania School of Engineering and Applied Science in the AleLab group supervised by Prof. Alejandro Ribeiro. He received a M.Sc. cum laude (and recognized as a top 1.5% student) in Data Science and a Ph.D. cum laude in Information and Communication Technologies, both from Sapienza University of Rome, and both under the supervision of Prof. Paolo Di Lorenzo. Dr. Battiloro’s research interests include theory and methods for topological signal processing and deep learning, to which he has made pioneering contributions, AI for healthy climate adaptation, and distributed stochastic optimization. He has over 40 publications, including papers published in top-tier journals (e.g., IEEE Transactions on Signal Processing, Journal of Machine Learning Research, IEEE Transactions on IoT, and IEEE Transactions on Green Communications and Networking) and conferences (e.g., ICLR, ICML, ICASSP, NeurIPS, and IJCNN). Claudio received different awards, such as the IEEE SPS Italian Chapter Best M.Sc. Thesis Award (2020), the GTTI Best Ph.D. Thesis Award (2024), and the "Elio Di Claudio" Best Ph.D. Thesis Award (2025).

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