From Real Systems to AI Solutions: Learning for Spatiotemporal Dynamics

Real-world spatiotemporal systems (e.g., transportation and maritime networks) produce heterogeneous, evolving data with strong spatial dependencies and long-range temporal dynamics. This half-day workshop connects operational challenges with modern learning methods—such as graph and sequence models, state-space approaches, and constraint-aware learning—to support robust prediction and decision-making in complex real systems.

• Spatiotemporal Modeling and Learning
  • Spatiotemporal deep learning for transportation, maritime, aviation
  • Long-range temporal dependency modeling in large-scale dynamic systems
  • Multi-resolution and multi-scale spatiotemporal representations
• Sequence, State-Space, and Generative Models
  • State-space and selective state-space models for spatiotemporal data
  • Sequence modeling beyond attention (e.g., efficient recurrent and hybrid architectures)
  • Generative and diffusion models for spatiotemporal forecasting, simulation, and data recovery
  • Probabilistic and uncertainty-aware spatiotemporal modeling
• Graph, Tensor, and High-Order Representations
  • Graph and hypergraph neural networks for spatiotemporal systems
  • Tensor-based modeling and factorization for multi-modal spatiotemporal data
  • High-order interaction modeling across space, time, and modalities
• Geometric and Topological Learning
  • Geometry-aware and manifold-based spatiotemporal learning
  • Non-Euclidean representations for spatial networks
  • Topology-informed learning and persistence-based representations
• Physics-, Constraint-, and Knowledge-Aware Models
  • Physics-informed and rule-consistent learning for spatiotemporal systems
  • Constraint-aware neural architectures for operational decision support
  • Integration of domain knowledge into data-driven spatiotemporal models
• Efficiency, Scalability, and Deployment
  • Computationally efficient spatiotemporal models for large-scale systems
  • Memory- and energy-efficient learning architectures
  • Model compression, approximation, and scalable training strategies
• Real-World Data, Systems, and Decision Support
  • Learning from noisy, incomplete, or heterogeneous spatiotemporal data
  • AI for operational decision-making in transportation and maritime systems
  • Case studies, benchmarks, and real-world deployments

• Workshop Paper Submission Deadline: 6 June 2026
• Workshop Reviews Deadline: 4 July 2026
• Notification of Acceptance or Rejection (Hard Deadline): 10 July 2026
• Camera-Ready Workshop Paper Submission Deadline (Hard Deadline): 18 July 2026

All deadlines and time zones will be announced on the official workshop page.

Submission site: https://cmt3.research.microsoft.com/ICANN2026

Submission format: Springer LNCS (Overleaf template)

Submission length: full papers (up to 12 pages)

This workshop uses a double-blind review process.

Accepted papers that comply with the ICANN 2026 regular paper guidelines will be published in the ICANN 2026 Workshop Proceedings.

All papers submitted to the workshop “From Real Systems to AI Solutions: Learning for Spatiotemporal Dynamic” must be submitted via Microsoft CMT through the ICANN 2026 submission portal. Authors are required to submit under the Workshops track and to select “From Real Systems to AI Solutions: Learning for Spatiotemporal Dynamic” as the target workshop. Please note that each workshop is reviewed and managed independently, and submissions made to the wrong workshop may not be considered. This workshop accepts full papers only under the standard ICANN 2026 format. Extended abstracts are not accepted.

Time	Session	Description
13:30 – 13:35	Opening Remarks	Welcome and workshop overview
13:35 – 14:00	Keynote Talk 1	Invited keynote (20 min presentation + 5 min Q&A) Real-world challenges in transportation and maritime systems (proposed)
14:00 – 14:15	Invited Talk 1	Invited expert presentation Operational challenges and datasets from real-world spatiotemporal systems (proposed)
14:15 – 15:00	Regular Paper Presentations I	3 regular papers × 15 min each
15:00 – 15:30	Tea Break & Poster Session	Coffee/tea break; poster presentations and informal discussion
15:30 – 15:55	Keynote Talk 2	Invited keynote (20 min presentation + 5 min Q&A) AI-driven spatiotemporal modeling for real systems (proposed)
15:55 – 16:10	Invited Talk 2	Invited expert presentation Deployment lessons and AI solutions in practice (proposed)
16:10 – 16:55	Regular Paper Presentations II	3 regular papers × 15 min each
16:55 – 17:00	Closing Remarks	Summary and wrap-up