Graph Deep Learning for Irregular Spatiotemporal Data
Faculty of Informatics - Academic Studies Administration
Date: 29 July 2025 / 14:00 - 17:00
USI East Campus, Room D0.02
You are cordially invited to attend the PhD Dissertation Defence of Ivan Marisca on Tuesday 29 July 2025 at 14:00 in room D0.02.
Abstract:
Irregular spatiotemporal data – with observations acquired unevenly across space and time – are common in real-world applications such as climate modeling, social media analytics, and transportation systems. In these settings, capturing underlying structural dependencies is critical for effective pattern recognition and learning. While the existing literature has predominantly focused on regularly sampled data, irregular sampling introduces significant challenges that require novel methodological approaches. My doctoral research aims to advance predictive modeling for irregular spatiotemporal data through a graph-based representation, focusing specifically on imputation, regularization, and prediction across both temporal and spatial dimensions. Graphs naturally lend themselves to modeling irregularities, with edges encoding relationships between data-generating processes, making graph deep learning a suitable and powerful processing framework. Imputation and regularization methods are proposed to reconstruct missing observations and improve data quality by enforcing spatiotemporal consistency. Prediction approaches are explored to forecast future observations by leveraging underlying dependencies. A theoretical analysis of information propagation in spatiotemporal architectures completes the thesis, providing insights and guidelines for the design of future models.
Dissertation Committee:
- Prof. Cesare Alippi, Università della Svizzera italiana, Switzerland (Research Advisor)
- Prof. Luca Maria Gambardella, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Marc Langheinrich, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Xiaowen Dong, University of Oxford, UK (External Member)
- Prof. Paolo Frasconi, University of Firenze, Italy (External Member)
- Prof. Marco Gori, University of Siena, Italy (External Member)