Professor with Università della Svizzera italiana and Politecnico di Milano
IEEE Fellow - ELLIS Fellow
His research focuses on graph-based learning, learning in non-stationary environments, lifelong learning, intelligence in embedded, cyber-physical systems and the Internet-of-Things.
His work is focused on the energy efficiency and reliability of Smart Grids.
His research focuses on learning in non-stationary environments with spatiotemporal and graph data.
He is interested in relational inductive biases for the processing of spatiotemporal data.
His studies concern the analysis and prediction of irregular spatiotemporal data.
Working on object detection and computer vision.
He studies spatiotemporal data processing using graph latent spaces.
His research focuses on graph-based prediction and control in reinforcement learning.
He works on applied research projects. His focus is on time series forecasting for the energy industry.
His research focuses on machine learning for hydrology.
Assistant professor at the University of Manitoba, Winnipeg (Canada)
His research interests lie at the intersection of machine learning and complex dynamical systems, with particular emphasis on graph-based methods.
Filippo Maria Bianchi
Associate professor at the Arctic University of Norway, Tromsø (Norway)
His research covers deep learning in recurrent neural networks, graph neural networks, time series analysis, and reservoir computing.
- Pietro Verzelli, Post-doc Researcher at Johannes Gutenberg University Mainz.
Ph.D., relationships between dynamical systems and machine learning. (2017-2022)
- Daniele Grattarola, Research Scientist at Isomorphic Labs.
Ph.D., graph neural networks for dynamical systems and computational biology. (2017-2021)
- Alberto Gasparin, Applied Scientist at Amazon Research (Berlin).
Politecnico di Milano, Milan (Italy), deep learning prediction and smart grids. (2017-2021)