Workshop on Cognitive Assistants and Data Fusion (CogniBlend)

Scope

CogniBlend brings together researchers and practitioners working on cognitive assistants and data fusion for intelligent decision-making in complex, real-world environments. The workshop emphasizes (i) architectures that integrate heterogeneous data (signals, images, text, sensors, distributed sources), (ii) learning and reasoning under uncertainty, and (iii) practical deployments in domains such as healthcare robotics, precision agriculture, and digital twins. 

A key goal is to align foundational methods (uncertainty handling, aggregation theory, perceptual comparison, multi-agent decision-making) with applied perspectives (robust learning pipelines, privacy-preserving collaboration, and operational constraints at the edge).

Key Themes

• Cognitive Assistants: AI systems that augment human capabilities and decision-making, including multi-modal assistants, agentic workflows, and context-aware interaction.
• Data Fusion: Methods for integrating heterogeneous data sources, handling uncertainty, and producing explainable, actionable outputs.

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Topics

We solicit contributions including (but not limited to):

• Federated Learning and privacy-preserving ML (edge/IoT, cross-silo, cross-device)
• Data fusion pipelines (multi-sensor, multi-modal, temporal fusion, alignment)
• Uncertainty quantification, aggregation theory, and robust decision-making
• Perceptual comparison and computational models of perception
• Digital twins and agent-based simulation for intelligent services
• Robotics in healthcare (triage, monitoring, assistive robotics) and HRI
• Precision agriculture: sensing, decision support, and field robotics
• Explainable AI for image classification and multi-modal inference
• Swarm intelligence and collective decision-making
• Reinforcement learning in multi-agent environments
• Real-world case studies, benchmarks, datasets, and deployment lessons