The goal of our research in Complex Networks is to extract useful and actionable information from the deluge of multi-modal, heterogeneous and streaming data generated by interconnected smart devices, platforms and systems.  In a network of networks (NoN) framework, we first develop techniques and models to discover and predict the underlying dynamic relationships and structure of network entities (smart devices, platforms, systems, and people).  We then develop algorithms to analyze and exploit the complex network structure and relationships, as well as methods to influence the network towards desirable outcomes.  We are applying our research to cyber-security, vehicle/platform health management, intelligence, surveillance, and reconnaissance (ISR), electronic warfare and social behavior analysis.

R&D Capabilities
Mathematical Data Modeling Analytics & Exploitation

• Network Science

- Discovery, controllability, adaptation
- Game theory
- Information dynamics
- Percolation, contagion theory

• Compressive sensing

• Sparse low rank representations

• Manifold and transductive learning

• Data alignment

• Early warning signals

• Fault detection, prediction, attribution

• Root cause analysis

• Geolocation

• Recognition & Tracking

• Collaborative Fusion