Filippo Maria Bianchi

Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society.

• Department: Department of Mathematics and Statistics
• School: Faculty of Science and Technology
• University: UiT The Arctic University of Norway
• Adjunct Position: Senior Researcher, NORCE

Education:

• Bachelor’s in Computer Engineering, Sapienza University of Rome
• Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012)
• PhD in Machine Learning, Sapienza University of Rome

His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting, with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability.

The recent publications reflect a strong trend in graph neural networks—particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing. His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments.

Scientific Affiliations and Leadership:

• Vice-Chair, IEEE Task Force on Learning for Structured Data
• Member, ELLIS Society
• Co-founder, Northernmost Graph Machine Learning group
• Member, IEEE Task Force on Reservoir Computing
• Visiting Professor, Politecnico di Milano (2024–2025)

He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery. His work is supported by open-source implementations and reproducible research practices.

Laboratories and Research Groups:

• Northernmost Graph Machine Learning group (co-founder)
• ARC Research Group, UiT
• Graph Machine Learning Group, Lugano