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
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Sep 2023
Our paper Taming Local Effects in Graph-based Spatiotemporal Forecasting (Cini et al.) has been accepted at NeurIPS 2023! -
Aug 2023
Our paper Sparse Graph Learning from Spatiotemporal Time Series (Cini et al.) has been published in JMLR! -
May 2023
We uploaded 8 new preprints about our latest research! Check them out: learning graph structures from data [1,2,3], state-space modeling [4,5], spatio-temporal time-series processing [6,7], generative models [8]. -
May 2023
Our tutorial Graph-based Processing of Spatiotemporal Time Series has been accepted to ECML PKDD 2023 (Sep. 18-22). -
Jan 2023
Submit your work to our special sessions Graph Representation Learning (ESANN 2023, Oct. 4-6) and Deep Learning for Graphs (IEEE IJCNN 2023, July. 18-23).
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.