Filippo Guerranti @ NeurIPS25

Our #NeurIPS2025 paper, NicheFlow, is out. In his thread, @k_sakalyan walks through how we model the evolution of cellular microenvironments in spatial transcriptomics. Worth reading if you want the details. Thanks @ale__palmaa @fabian_theis @guennemann.

This is happening today: Wed, Dec 3, 2025 • 4:30 PM – 7:30 PM PST Exhibit Hall C,D,E #3715 Looking forward to the discussions!

Excited to share that my first PhD paper, which introduces a new diffusion model for relational databases, has been accepted to #NeurIPS2025! We will be presenting it this week in San Diego. ☀️🌴 Joint work with @ludke_david, @SchwinnLeo, and @guennemann. 🧵 1/

Always happy to chat about graphs, generative models, or spatial omics while in San Diego. 🌊 See you there! 1️⃣openreview.net/forum?id=d2Eou… 2️⃣openreview.net/forum?id=5ofJy… 3️⃣ arxiv.org/abs/2509.06743 #MachineLearning #GraphML #GenerativeModels #SpatialTranscriptomics

3️⃣ #LRGWN: Long-Range Graph Wavelet Networks We combine local polynomial filters with global spectral filters to efficiently model long-range interactions in graphs. 📍 NPGML Workshop (Hall F) | Poster #26 🕒 Sun 7 Dec, 9:00–9:30 PST (w/ F. Forte @geisler_si @guennemann)

Heading to San Diego for #NeurIPS2025! 🌴☀️ I’ll be presenting 3 recent papers covering generative models for hierarchies, spatiotemporal tissue dynamics, and long-range graph learning. If you're around, drop by and say hi! 👋 Here’s the schedule 🧵👇

Can we actually tell when LLMs know or don’t know? For questions with a single answer, that works. But once ambiguity enters - several answers are correct - current methods collapse, confusing model with data uncertainty. w/ @dfuchsgruber @TomWollschlager @guennemann [1/4]🧵

The hunt for increased WL expressivity has led to many new GNNs but limited real-world success. So what are we missing? Can we find a better objective? We answer these questions in our new paper: arxiv.org/abs/2509.01254 Joint work /w @TomWollschlager @guennemann 🧵 (1/6)

Quick clarification on the first figure: - Linearization does not preserve structure, but does not require model changes. - Graph encoders preserve structure, but typically require model changes.

Injecting structure into LLMs with no changes to the architecture? SAFT🧃 Structure-aware LLM fine-tuning for AMR-to-text. New SOTA + no model changes! 📍#SKnowLLM, KDD, Toronto 🇨🇦 🗓️ Aug 4, 1–5pm · Room 717 📄 arxiv.org/abs/2507.13381 w/ R. Kamel, @geisler_si, @guennemann 1/5

5/5 If you’re working on LLMs for structured data (graphs, trees, AMRs, and so on) stop by! We’ll be at the KDD Workshop on Structure Knowledge for LLM #SKnowLLM 📍Toronto 🇨🇦 · MTCC · Room 717 🕐 August 4th, 1–5pm SAFT: Structure-Aware Fine-Tuning for LLMs

4/5 🏆 Results? SAFT improves across models and dataset scales: · +3.5 BLEU vs prior SOTA (no extra data) · Improves generalization on long + complex graphs · Compatible with LLaMA, Qwen, Gemma, etc.

Do you think your LLM is robust?⚠️With current adversarial attacks it is hard to find out since they optimize the wrong thing! We fix this with our adaptive, semantic, and distributional objective. By @guennemann's lab & @GoogleAI, w/ @cais support Here's how we did it. 🧵

Monday with @n_gao96 in the reading group "Learning Equivariant Non-Local Electron Density Functionals" arxiv.org/abs/2410.07972 Join us on zoom at 9am PT / 12pm ET / 6pm CET: portal.valencelabs.com/logg

Deep learning with differential privacy can protect sensitive information of individuals. But what about groups of multiple users? We answer this question in our #NeurIPS2024 paper arxiv.org/abs/2403.04867 Joint work w/ @mihail_sto @ArthurK48147 @guennemann. #Neurips (1/7)

Excited to share that my master's thesis on "molecular graph generation in latent Euclidean space" was accepted at @GRaM_workshop and selected for an oral presentation. If you are at @icmlconf on Saturday, make sure to stop by.