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Archive / INF Seminars / INF_2024_09_20_AlessioMicheli

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	A Journey Through the Deep Learning for Graphs

Friday

20.09

USI East Campus, Room C1.02

15:30 - 16:30


	Alessio Micheli University of Pisa

Abstract: Although the investigation on learning in structured domains started in the late 90’s, recently deep learning for graphs has attracted tremendous research interest and increasing attention for applications. Indeed, graphs are powerful and flexible tools to represent at different levels of abstraction relationships among data. Not surprisingly the area of applications includes many fields, from biology, chemistry, network science, computer vision, natural languages and many others. On the other hand, extending the data domain to graphs opens new challenges to the field of deep learning (DL). The talk will briefly introduce the area of DL for graphs. We will also discuss advanced topics and current open issues, including examples of recent progresses of my research group, with an emphasis on the efficiency issue and on the interplay between depth of the models and complex data representation learning.

Biography: Alessio Micheli is Full Professor at the Department of Computer Science of the University of Pisa, where he is the head and scientific coordinator of the Computational Intelligence & Machine Learning Group (CIML), part of the CLAIRE-AI.org Research Network. His research interests include machine learning, neural networks, deep learning, learning in structured domains (sequence, tree, and graph data), recurrent and recursive neural networks, reservoir computing, and probabilistic and kernel-based learning for nonvectorial data, with an emphasis on efficient neural networks for learning from graphs. Prof. Micheli is the national coordinator of the "Italian Working group on Machine Learning and Data Mining" of the Italian Association for Artificial Intelligence and he has been co-founder/chair of the IEEE CIS Task Force on Reservoir Computing. He is an elected member of the Executive committee of the European Neural Network Society – ENNS. He serves as an Associate Editor for Neural Networks and IEEE Transactions on Neural Networks and Learning Systems.