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
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Aug 2024
Our survey on GNNs for time series has been accepted at IEEE TPAMI! -
May 2024
Two papers accepted at ICML 2024! Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting (Cini et al.) and Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling (Marisca et al.). -
Apr 2024
Paper Temporal Graph ODEs for Irregularly-Sampled Time Series has been accepted at IJCAI 2024! -
Jan 2024
Our paper Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations has been accepted at ICLR 2024! -
Dec 2023
Submit by Jan. 15 to our special sessions Deep Learning for Graphs at IEEE WCCI 2024 in Yokohama, Japan (Jun. 30-Jul. 5).
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.