Organized by the IEEE Computational Intelligence Society Shenzhen Chapter.

Activity Aim & Talk

Irregular spatiotemporal data consist of observations collected asynchronously across different times and spatial locations. Unlike regular data, their inherent irregularity makes traditional methods designed for discrete-time sequences and Euclidean spaces partly ineffective. In this talk, we explore how graph deep learning leverages the existence of relational dependencies to tackle both interpolation (imputation) and extrapolation challenges. We will cover techniques for reconstructing missing data from a limited set of observations by enforcing spatiotemporal consistency, as well as forecasting methods for predicting future values from sparse inputs.

Meet the Speaker

CESARE ALIPPI is Professor with the Università della Svizzera italiana (Switzerland) and Professor with the Politecnico di Milano (Italy); he is visiting Professor at the Guandong University of Technology (China) and Consultant Professor at the Northwestern Polytechnic of Xi’An (China). He has been a visiting researcher/professor at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), A*STAR (SIN), U.Kobe (Japan). Alippi is an IEEE Fellow, ELLIS Fellow, INNS Fellow and AAIA Fellow, Past Board of Governors member of the International Neural Network Society, Past member of the Administrative Committee of the IEEE Computational Intelligence Society (CIS), Past Board of Directors member of the European Neural Network Society, Past Vice-President education of the IEEE Computational Intelligence Society, Associate Editor of Proceedings of IEEE and other journals, Past Associate editor of the IEEE Computational Intelligence Magazine, the IEEE-Transactions on Instrumentation and Measurements, IEEE-Transactions on Neural Networks, IEEE-Transactions on Emerging Topics in Computational Intelligence and member and chair of many IEEE committees.

In 2024 he received the IEEE CIS Enrique Ruspini Meritorious Service Award, the 2018 IEEE CIS Outstanding Computational Intelligence Magazine paper award, the 2016 Gabor Award from the International Neural Networks Society and the Outstanding Transactions on Neural Networks and Learning Systems Paper Award from the IEEE Computational Intelligence Society; in 2013 the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award. Current research activity addresses graph-based learning, adaptation and learning in non-stationary environments and Intelligence for embedded, cyber-physical systems and IoT. For the graph-based learning research please refer to http://gmlg.ch. He holds 8 patents, has published one monograph book (translated in Chinese), 7 edited books and about 250 papers in international journals and conference proceedings.