Call for Papers

Workshop Scope

Scale-to-See establishes a dedicated forum at the intersection of high-performance computing (HPC), artificial intelligence (AI), scientific imaging, and high-dimensional spatiotemporal data. While AI for science, large-scale simulation, and imaging are each active areas, there is currently no cohesive venue focused on AI methods and systems designed to operate at extreme HPC scale for imaging and spatiotemporal inference.

Scientific imaging and spatiotemporal data—arising in domains such as medical imaging, climate and weather, earth systems, materials science, manufacturing, bioengineering, and cosmology—are increasingly high-dimensional, multimodal, and time-evolving. Advancing these fields requires AI that is not only accurate, but also scalable, physics-aware, reproducible, and deployable on leadership-class HPC systems.

This workshop aims to answer this central question:
How do we design AI methods and systems that operate natively at HPC scale, respect physical constraints, and transform scientific imaging and spatiotemporal inference?

Topics

Topics include but are not limited to:

Core Theme 1: Computational Imaging and Observation
• Computational imaging and AI-driven inverse problems
• Neural imaging and learned image reconstruction
• Scientific sensing and observation systems
• Uncertainty quantification for imaging and inverse problems
• Multimodal sensing and data fusion

Core Theme 2: Scalable AI for Scientific Observation
• Foundation models for scientific imaging and spatiotemporal data
• Physics-informed machine learning and scientific AI
• Scientific representation learning and self-supervised learning
• Scientific forecasting, state estimation, and data assimilation
• Generative AI for scientific discovery

Core Theme 3: HPC and Systems
• AI-HPC co-design for scientific discovery
• HPC-aware AI methods for imaging and spatiotemporal learning
• Exascale AI workflows
• Large-scale distributed training and inference
• Efficient computing for scientific imaging and inverse problems

We especially welcome HPC and scalable AI methods that enable seeing observations beyond instrument limits for imaging and spatiotemporal applications. 

Submission Format:

• Paper Length: 4–8 pages (excluding references)

• Format: SC Proceedings Template 

• The review process is double-blind, i.e. the names of the authors, reviewers, and area chairs are not revealed to each other. Papers must thus be properly anonymized before submission. 

• Domain conflicts: To avoid conflict of interest among the authors, reviewers and meta-reviewers, all co-author information and a complete and accurate list of domain conflicts must be properly entered in the SC submission site by the submission deadline. 

Paper Publication:

Accepted papers will be published in SC'26 proceedings as workshop papers. Among the accepted workshop papers, Scale-to-see will give one award to the best paper with certification.