We are tackling a fundamental challenge: enabling robust and secure data fusion in environments where traditional sensors are being replaced by intelligent computers, robotics, and AI-driven agents. As computing and AI technologies advance, integration becomes critical—not just at the algorithmic level but across heterogeneous systems.

Our applied research focuses on building integration frameworks that combine:

• Large Language Models (LLMs) for reasoning and adaptive decision-making.
• Retrieval-Augmented Generation (RAG) for dynamic knowledge access.
• Robotics and autonomous platforms for real-world sensing and actuation.
• Edge and cloud computing architectures for scalable deployment.

Key applied challenges we address:

• System interoperability between robotics, AI models, and legacy infrastructure.
• Secure communication protocols for multi-agent collaboration.
• Privacy-preserving data fusion across distributed environments.
• Real-time consensus and reliability in mission-critical applications.
• Federated learning and multi-party computation for collaborative intelligence.

Our goal is to create end-to-end integration systems that allow intelligent agents—whether robots, sensors, or AI models—to work together seamlessly, securely, and efficiently in complex environments.