Daniele Zambon

Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs.

• PhD: Informatics, Università della Svizzera italiana (USI), 2022
• Master’s & Bachelor’s: Mathematics, University of Milan, Italy
• Visiting Researcher: University of Florida, University of Exeter
• Internship: STMicroelectronics, Italy

His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning, learning in non-stationary environments, and time series analysis. He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs.

The recent publications highlight a clear trend toward temporal and dynamic graph modeling, especially for time series forecasting and irregularly sampled data. There is a growing focus on generative and foundation models for graphs, uncertainty-aware learning, and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring.

• Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2)
• PhD thesis featured in D22 Excellent Computer Science Dissertations (2022)
• Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)
• Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN

Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field.

He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs, fostering international collaboration and setting research agendas in the domain of graph-based AI.