Ivan Marisca

Tomorrow at 5 pm CET, I'll be presenting my latest paper "Over-squashing in Spatiotemporal Graph Neural Networks" (NeurIPS 2025) at the @tempgraph_rg (w/ @jacobbamb). Check it out: arxiv.org/abs/2506.15507

The Temporal Graph Learning Reading Group is back from Winter Break ❄️ See you on Feb 5th, 11am EST @IvanMarisca will present: Over-squashing in Spatiotemporal Graph Neural Networks (NeurIPS 2025) more info on our website

It took a European journalist to finally ask this neo-nazi monster the kind of questions American journalists have failed to ask her on a daily basis for almost a year now.

It truly is a failure of our society that a man worth $640 billion is allowed to spend his days broadcasting lies and conspiracy theories to incite hate to hundreds of millions of people on a platform where he controls what you see, when you see it, completely unchecked.

Tomorrow, I’ll present our work “What Matters in Deep Learning for Time Series Forecasting?” at the #EurIPS workshop on Benchmarking and Evaluating AI. Come by for a chat if you’re around! Many thanks to @andreacini1994 , @IvanMarisca , and Cesare Alippi

🎉 Honored to receive the Informatics Europe Best Dissertation Award 2025 for my PhD thesis! Beyond grateful to my advisor Cesare, my amazing collaborators, and all the people at @USI_university! 🙏🙏 🔗 informatics-europe.org/news/968-usi-g…

How to tamper with a gas meter to pay lower bills? This is the story of how a supposedly aligned open source LLM, perhaps not even knowing how to do it, will give you the right instructions. (1/3) arxiv.org/abs/2510.20075

🚨 ICML 2025 Paper 🚨 "On Measuring Long-Range Interactions in Graph Neural Networks" We formalize the long-range problem in GNNs: 💡Derive a principled range measure 🔧 Tools to assess models & benchmarks 🔬Critically assess LRGB 🧵 Thread below 👇 #ICML2025

🚨 Exciting news! We released 🎱 tgp (Torch Geometric Pool), the library for pooling in Graph Neural Networks. 🚀 Get started with our tutorials: torch-geometric-pool.readthedocs.io/en/latest/tuto… With @IvanMarisca and Carlo Abate. #GraphML #GNN #Pooling #Pyg

Freshly accepted as a tutorial paper in ACM Computing Surveys! 🥳 Check out the updated version: dl.acm.org/doi/abs/10.114… cc @IvanMarisca @dan_zambon

🔥 Bayesian nonparametrics meets pooling in GNNs! 🔥 This is the first clustering-based pooling method that learns and dynamically adapts the size of the pooled graph to input and downstream task. 📄 paper: arxiv.org/abs/2501.09821 💻 code: github.com/NGMLGroup/Baye…

3. Graph Deep Learning for Time Series Processing: Forecasting, Reconstruction, and Analysis by @andreacini1994, @IvanMarisca, @dan_zambon 4. Integrating Knowledge Graphs and Large Language Models for Advancing Scientific Research by @qzhang_cs, @ChenJiaoyan1, @mengzaiqiao

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”

A pleasure co-advising such great students 👏

In a few hours, I will present HiGP at #ICML2024 😊 If you are interested in how to exploit graph-based representations and hierarchical clustering to forecast correlated time series, come and say hi at poster #907 from 1:30 to 3:00 PM 😉

@FilippoMariaBi1 @alfcnz looking forward to your feedback on our poster! 😉

Come to visit me and @IvanMarisca tomorrow at the first poster session (11:30-13:00 - Hall C) as we present our paper #1016! #ICML2024

@claudiogallicc1 @FilippoMariaBi1 Thanks Claudio, see you at the conference!

@IvanMarisca @FilippoMariaBi1 Wow!!!! Huge congrats, Ivan!

🧵 Ready for #ICML2024! This year me and @FilippoMariaBi1 present a method to forecast correlated time series with missing data. We compute a hierarchy of multi-scale spatiotemporal representations and adaptively combine them conditioned on the missing data pattern👇

@FilippoMariaBi1 4/ Two bonus features: ⚡️ The scalability is given by factorized time-then-space processing and precomputed downsampling operators. 🔍 Gain insights on the scales through the scores! 👇