Daniele Zambon

Hello! My name is Daniele, I am a post-doc at the Swiss AI Lab 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.


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Short bio

I obtained my Ph.D. from USI (CH) in 2022. My research dealt with statistical tests for anomaly and change detection, graph representation learning, and learning in non-stationary environments. You can find a list of my publications here.

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 pantent. I received Master’s and Bachelor’s degrees in mathematics from the Università degli Studi di Milano (IT) focusing on approximation theory and mathematical statistics.

I have published in and have been reviewer for top-tier journals and conferences of the field, including JMLR, IEEE TPAMI, IEEE TNNLS, IEEE TSP, NeurIPS, ICLR, and ICML; I was certified Outstanding Reviewer of 2022 by the IEEE Computational Intelligence society and Top 33% Reviewer for ICML 2020. I hold a patent. I have organized special sessions and tutorials at international conferences on graph deep learning.


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


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 .