Graph Machine Learning Group
Graph machine learning • Non-stationary environments • Spatiotemporal data • Reinforcement learning • Dynamical systems
About GMLG
We are a research team part of the Swiss AI Lab (IDSIA) at Università della Svizzera italiana, Lugano, Switzerland. The group is led by Prof. Cesare Alippi.
Meet our teamNews
- Our paper Scalable Spatiotemporal Graph Neural Networks (Cini et al.) won the best paper award at the NeurIPS 2022 Temporal Graph Learning Workshop!
- Our paper Scalable Spatiotemporal Graph Neural Networks (Cini et al.) has been accepted at AAAI 2023!
- Our papers AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs (Zambon & Alippi) and Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations (Marisca et al.) have been accepted at NeurIPS 2022!
Research
Our research focuses on graph machine learning, non-stationary environments, dynamical systems, and reinforcement learning.
We also apply machine learning in many diverse fields, including neuroscience, power grids, chemistry, and agriculture, among many others.
Open Source
Our group is active in open source software development and we maintain several Python libraries based on our research. Check out also the group GitHub page for code related to our papers.
The development of Spektral and CDG was supported by project ALPSFORT (200021 172671) of the Swiss National Science Foundation.