Extended Abstracts
Up to 4 pages (excluding references and an optional appendix), prepared using the NeurIPS 2026 style file; non-archival submissions allowed.
Machine Learning/CORE Unranked
ML4SpatialBio 2026
NeurIPS Workshop on Machine Learning for Spatially Resolved High-dimensional Biology
ML4SpatialBio 2026 is a one-day NeurIPS workshop focused on advancing machine learning methods for spatially resolved high-dimensional biology. It brings together ML researchers and experimental biologists to address challenges in analyzing spatial multiomics and tissue-scale data, with an emphasis on developing novel, interpretable, and scalable models that integrate geometric, multimodal, and biological context.
Key deadlines
Verified September 9, 2026Full paper
September 6, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
September 6, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Research areas in scope
01
Topics of Interest
Learning biologically meaningful spatial representations that hold across scales, from molecules to niches to organsIntegrating transcriptomic, proteomic, imaging, and clinical modalities into a single coherent modelModeling cell-cell communication and tissue dynamics over timeBuilding interpretable and uncertainty-aware spatial models that biologists can trustDesigning benchmarks, datasets, and evaluation standards specific to spatial tasksDeveloping novel ML and foundation models for spatial biology by integrating prior biological knowledge and inductive biases to improve interpretability and generalization across platforms, tissues, and speciesHandling data sparsity, noise, batch effects, and limited annotationsScaling learning to atlas-scale and whole-slide datasets
Policies worth checking twice
- Submissions must be anonymized for double-blind review (no author names or affiliations)
- Submissions are non-archival, allowing concurrent submission to other venues
- Papers must use the official NeurIPS 2026 LaTeX style file with anonymous submission option
- Each submission receives three reviews from a program committee drawn from both ML and biology communities
- Organizers will not review submissions from their own institution or direct collaborators
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026