Geometric & Graph Generative AI

Sun, 25 Jan 2026 00:00:00 +0000

Overview

We investigate the fundamental limits of learning and information processing for geometric data. Our goal is to develop theoretically grounded generative models that can handle the complexity of 3D structures and molecular graphs.

Active Projects

1. Autoregressive Expansion for Latent Graph Diffusion

Goal: Extend Latent Graph Diffusion (LGDC) by introducing an autoregressive expansion mechanism. Details: Instead of expanding all nodes in a single step, this project generates fine-level structure iteratively, allowing local decisions to be conditioned on previously generated substructures.

2. Grounding Geometric Generative Models

Goal: Leverage discrete differential geometry to build better generative models. Details: We view graphs as samples from an underlying manifold. This project derives new families of diffusion and flow-based models grounded in curvature approximations and stochastic differential equations on manifolds.

LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening
N Osman, K Jiang, D Buffelli, X Dong, L Toni. NeurIPS 2025 Workshop.

Effects of Random Edge-Dropping on Over-Squashing in Graph Neural Networks
J Singh, K Jiang, B Paige, L Toni. NeurIPS 2025.

Bures-Wasserstein Flow Matching for Graph Generation
K Jiang, J Cui, X Dong, L Toni. Submitted to ICLR, 2025.

From In Silico to In Vitro: Evaluating Molecule Generative Models
N Osman, V Lembo, G Bottegoni, L Toni. NeurIPS 2025 AI4Science Workshop.

Midi: Mixed graph and 3d denoising diffusion for molecule generation
C Vignac, N Osman, L Toni, P Frossard. ECML PKDD 2023.

Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
K Jiang, B Tang, X Dong, L Toni. AISTATS 2025.

Graph ML for Science

Sun, 25 Jan 2026 00:00:00 +0000

Overview

Biology and Chemistry are fundamentally relational—molecules are graphs of atoms, and cellular functions rely on complex interaction networks. We develop geometric deep learning methods to model, generate, and understand these structures.

Active Projects

1. Generative Biology & Drug Discovery

Goal: Move from “In Silico” generation to “In Vitro” validation. Details: We are building generative models (like MiDi and LGDC) that can design novel molecules with specific 3D geometries and chemical properties. A key focus is bridging the gap between computational metrics and actual wet-lab success rates.

2. Transcriptomics & Interaction Networks

Goal: Decode the language of the cell. Details: Using Graph Neural Networks (GNNs) and Graph Signal Processing, we model gene regulatory networks and protein-protein interactions. Our aim is to infer causal relationships in transcriptomic data to identify potential therapeutic targets.

From In Silico to In Vitro: Evaluating Molecule Generative Models
N Osman, V Lembo, G Bottegoni, L Toni. NeurIPS 2025 AI4Science Workshop.

LGDC: Latent Graph Diffusion via Spectrum-Preserving Coarsening
N Osman, K Jiang, D Buffelli, X Dong, L Toni. NeurIPS 2025 Workshop.

Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
K Jiang, B Tang, X Dong, L Toni. AISTATS 2025.

Graph Learning | Learning And Signal Processing

Geometric & Graph Generative AI

Overview

Active Projects

1. Autoregressive Expansion for Latent Graph Diffusion

2. Grounding Geometric Generative Models

Related Publications

Graph ML for Science

Overview

Active Projects

1. Generative Biology & Drug Discovery

2. Transcriptomics & Interaction Networks

Related Publications