Nuclear & Biological Forensics Attribution

IDSAI applies machine learning, scientific computing and evidence-centered AI to complex attribution problems. The goal is to help experts integrate heterogeneous measurements, identify meaningful signatures, quantify uncertainty and communicate defensible conclusions.

The mission challenge

Attribution problems may involve sparse observations, changing conditions, multiple mechanisms, incomplete reference data and high consequences for error. Useful AI must preserve scientific traceability, distinguish evidence from inference, communicate uncertainty and remain subordinate to expert judgment and established analytical protocols.

Research priorities

• Multimodal fusion across spectra, imagery, sensor streams, laboratory measurements, documents and contextual data
• Signature discovery, representation learning and anomaly characterization
• Source attribution, similarity analysis and probabilistic evidence assessment
• Physics-, chemistry- and biology-informed machine learning
• Generative and simulation-based methods for rare-event and limited-data settings
• Retrieval-augmented scientific intelligence with provenance-aware evidence chains
• Interpretable models, calibrated uncertainty and sensitivity analysis
• High-performance workflows for large-scale simulation, search and ensemble analysis

Responsible research boundary

Public content must not disclose controlled, export-restricted, security-sensitive, proprietary or personally identifiable information. Website language should describe research methods and public outcomes without implying access to restricted data or operational authorities.

Projects and evidence

DYNAMIC MODULE: [CONTENT NEEDED: approved public projects, publications, datasets, facilities, partners and sponsor permissions]