Daniele Zambon

Hello! My name is Daniele, I am a post-doc at IDSIA, USI, in the Graph Machine Learning Group, and member of the IEEE Task Force on Learning for Graphs.

The focus of my research is graph representation learning, learning in non-stationary environments, and time series analysis.

name.surname@usi.ch

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News!

Short bio

I am a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Switzerland . I am member of the IEEE Task Force on Learning for Graphs and the Graph Machine Learning Group in Lugano.

I earned my Ph.D. in Informatics from USI (Jan ‘22), focusing on statistical tests for anomaly and change detection, graph representation learning, and learning in non-stationary environments. Prior to my PhD, I graduated with honors from the University of Milan (IT, Apr ‘16) with a degree in Mathematics specializing in approximation theory and mathematical statistics. During my doctoral studies, I have been visiting researcher at the University of Florida (US, Nov ‘19–Feb ‘20) working on kernel adaptive methods and at the University of Exeter (UK, Sep ‘17, Oct ‘18) exploring embeddings onto Riemannian manifolds. I have also been an intern at STMicroelectronics (IT, May ‘15–Apr ‘16, May ‘16–Sep ‘16) where I developed my Master’s thesis on sparse models for anomaly detection and co-authored a patent.

My work has been published in top-tier journals and conferences of the field, including JMLR, IEEE TPAMI, IEEE TNNLS, IEEE TSP, NeurIPS, ICLR, and ICML. I am associate editor for IEEE TNNLS. I hold a patent. I have co-organized special sessions and tutorials at international conferences on graph deep learning.

Publications

Please, find a list of my publications here and on Google Scholar .

Teaching

I regularly serve as a teacher in Master’s and Bachelor’s degree programs at USI, holding lectures, lab sessions, and examinations. Find a detailed list here .