I will be in Vancouver next week for #ICML2025! If you’re around, would love to catch up 😊 Also, if you’re interested in uncertainty quantification in time series forecasting, come say hi at poster E-1706 on Tuesday from 16:30 to 19:00!

🚨 Exciting news! We released 🎱 tgp (Torch Geometric Pool), the library for pooling in Graph Neural Networks. 🚀 Get started with our tutorials: https://t.co/Zhkp551VVv With @IvanMarisca and Carlo Abate. #GraphML #GNN #Pooling #Pyg

🚀 We just released the code for our new #ICML2025 paper "Relational Conformal Prediction for Correlated Time Series" by @andreacini1994 and co-authors! 💻 code: https://t.co/CK1JwPySdY 📑 paper: https://t.co/tSp5WD558q

Happy to share that CoRel has been accepted at ICML!🥳 CoRel quantifies uncertainty in correlated time series forecasting by leveraging graph deep learning operators. Check it out! 😎 W/ @alj_jenkins, Danilo, Cesare, and @FilippoMariaBi1 🔗 https://t.co/O2rWbeQAl6

Tomorrow (April 26th), I'll present our recent work on MAXCUT with GNNs for graph pooling at #ICLR2025 Stop by to say hi at Poster 215 from 10:00 to 12:30 ☺️

Join us to hear talks from great experts https://t.co/UdUdCle6dw @marinkazitnik @tolga_birdal @FilippoMariaBi1 Coralia Cartis, Marco Cuturi, Estelle Massart, @Pseudomanifold, Francisco Ruiz, Alice Barbara Tumpach

Not happy with the review process at #ICML2025. 6 reviews in 1 month are too much: review quality and engagement in the rebuttal will be low. Also, the 12 (!!) mandatory questions are often irrelevant. Why comment on the supplementary if a major flaw (e.g. plagiarism) is found?!

Big personal update 😊 I'm moving to @UniofOxford to work with @mmbronstein on my recently funded Swiss National Science Foundation project 🎉🎉 As part of the project, I'll also be collaborating with @FilippoMariaBi1 😃 Looking forward to exciting new research & collabs! 😎

🎉 Our paper was accepted to #ICLR2025! Project page: https://t.co/8I5NYl7Pax

🔥 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: https://t.co/SwGSHLyFzE 💻 code: https://t.co/WxjydvFaIz

Just published a new blog on 🎱 Pooling in #GraphNeuralNetworks! 💡Learn the fundamentals through this gentle introduction and discover how they can improve your GNN applications. 👉 Check it out here: https://t.co/IEmZVccC3N #GNN #MachineLearning #AI

Our new featured LoG2024 meetup will be held in the Artic❄️ Tromsø🇳🇴Meetup info: Date-> 27 Nov. 2024 Website->

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

🤔 How to interpret spatio-temporal data and deep learning models? 💡In our recent work with Michele Guerra and @s_scardapane we leverage Koopman theory to design an XAI framework for spatio-temporal GNNs. 📄 Preprint: https://t.co/cThmDCIgqM 💻 Code: https://t.co/N74aF8bCT8

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

🧵 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👇

🇫🇷 Next week I'll be in Paris attending #SIAMLA24. I'll give a talk about our recent work on Total Variation Graph Neural Networks, first presented at ICML last year https://t.co/oCBIfrHff7 Looking forward to discussions about graphs, machine learning, and optimization!

It was really a pleasure to be invited at the West Norway University of Applied Science as the opponent for the PhD thesis of Michele Gazzea. Excellent work, both the defendant and his supervisor @RezaArghandeh!

*Kolmogorov-Arnold Networks (KANs)* by @ZimingLiu11 et al. Since everyone is talking about KANs, I wrote some notes on Notion with a few research questions I find interesting. First time I do something like this, give me some feedback. 🙃 https://t.co/bNhaHLnxFI

🎉 The course "Time series analysis with Python" is out! 💡 Dive into a comprehensive introduction to time series analysis, filled with coding examples and exercises. 💻 Gain practical skills and master fundamental techniques. Check it out here: https://t.co/tkYxoiK7Am