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Andrea Dittadi

Collaborateur·rice de recherche - Helmholtz AI
Superviseur⋅e principal⋅e
Dhanya Sridhar
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Causalité
Modèles génératifs
Modèles probabilistes
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction
Vincent Pauline
Tobias Höppe
Kirill Neklyudov
Alexander Tong
Stefan Bauer
Andrea Dittadi
2025-11-30
arXiv (publié)
doi.org
arxiv.org
Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
Alessandro Palma
Till Richter
Hanyi Zhang
Manuel Lubetzki
Alexander Tong
Andrea Dittadi
Fabian J. Theis
Generative modeling of single-cell RNA-seq data is crucial for tasks like trajectory inference, batch effect removal, and simulation of real… (voir plus)istic cellular data. However, recent deep generative models simulating synthetic single cells from noise operate on pre-processed continuous gene expression approximations, overlooking the discrete nature of single-cell data, which limits their effectiveness and hinders the incorporation of robust noise models. Additionally, aspects like controllable multi-modal and multi-label generation of cellular data remain underexplored. This work introduces CellFlow for Generation (CFGen), a flow-based conditional generative model that preserves the inherent discreteness of single-cell data. CFGen generates whole-genome multi-modal single-cell data reliably, improving the recovery of crucial biological data characteristics while tackling relevant generative tasks such as rare cell type augmentation and batch correction. We also introduce a novel framework for compositional data generation using Flow Matching. By showcasing CFGen on a diverse set of biological datasets and settings, we provide evidence of its value to the fields of computational biology and deep generative models.
2025-04-22
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net