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.
The group was previously under the ALaRI institute.
Meet our teamNews
-
Jun 2025
Our paper Graph Deep Learning for Time Series Forecasting (Cini et al.) has been accepted to ACM Computing Surveys! -
May 2025
Our papers Learning Latent Graph Structures and their Uncertainty (Manenti et al.) and Relational Conformal Prediction for Correlated Time Series (Cini et al.) have been accepted at ICML 2025! -
Mar 2025
Our workshop Inductive Biases in Reinforcement Learning (IBRL) has been accepted to RLC 2025! Consider submitting your work. -
Feb 2025
Two new papers accepted at TMLR! Feudal Graph Reinforcement Learning (Marzi et al.) and On the Regularization of Learnable Embeddings for Time Series Forecasting (Butera et al.). -
Aug 2024
Our survey on GNNs for time series has been accepted at IEEE TPAMI!
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.