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 graph stream processing.

name.surname@usi.ch
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News!

• [9-2023] New paper putting together our knowledge on designing graph DL models for time series forecasting.
• [9-2023] Our study on modeling local effects in STGNNs has been accepted at NeurIPS ‘23.
• [9-2023] I have been selected as Outstanding Reviewer of 2022 for IEEE TNNLS.
• [8-2023] Our paper on graph learning from time series will be published in JMLR!
• [7-2023] Working on a survey on GNN architectures for time series analysis: check out our preprint GNN4TS.
• [6-2023] Our tutorial on graph deep learning for time-series processing has been accepted at ECML PKDD 2023!
• [5-2023] I’ve become member of the IEEE Task Force on Learning for Graphs.
• [3-2023] A bunch of preprints about Kalman filters [1], spatio-temporal models [2, 3, 4], and model optimality [5].
• [2-2023] Our special sessions on DL4G @ IEEE IJCNN 2023 and GRL @ ESANN 2023 will be held soon in Gold Coast (Queensland, AU) and Bruges (BE).
• [11-2022] Paper accepted @ NeurIPS ‘22. AZ-whiteness test: with it you can test the optimality of GNN’s.
• [11-2022] We have got funded by OCRE with cloud computing resources for a project in collaboration with UniPD.

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.

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 .