Artificial Intelligence and Advanced Decision Analytics

Through immersive labs and mentorship, personnel master real ML pipelines, ethics and federal compliance standards. Participants work with sandbox data, publish reproducible notebooks and earn certifications. Our programs develop internal champions who promote best practices, automate governance and maintain secure workflows, strengthening organizational readiness across leadership and staff.

The AI monitoring, testing and evaluation landscape is undergoing a transformative shift, driven by AI’s growing complexity and importance. Our research ensures AI remains reliable, fair and ethical throughout its lifecycle, with real-time tracking of performance, stability, bias and transparency using continuous data collection, anomaly detection and automated alerts.

Our approach incorporates comprehensive governance, red teaming and safety cases from concept to decommissioning. Our Responsible AI Framework aligns risks with National Institute Standards Technology (NIST), Department of Defense (DoD) and Office of Management and Budget (OMB) controls, defines measurable guardrails and automates evidence collection to ensure regulatory compliance, oversight and leadership confidence in lawful, ethical and resilient autonomous analytics.

Utilizing advanced reinforcement learning, sensor fusion and constraint-based planning, our agents perceive, decide and act independently. They reroute around failures, negotiate resources and adapt to mission changes without supervision. Deployed in logistics, robotics and unmanned systems, these solutions enhance agility, allowing humans to focus on collaboration and governance.

Our research integrates imagery, sensors, audio and text for comprehensive environmental understanding. Using self-supervised learning, efficient transformers and domain adaptation, they overcome occlusion, low-light and limited labels. Operators gain real-time analytics for inspection, security, disaster response and asset tracking to enhance situational awareness during connectivity or resource challenges.

Our interactive explainability suite combines prediction scores, counterfactuals and causal graphs to reveal why models behave as they do. Stakeholders can trace predictions to source features, evaluate metrics and simulate policy impacts in real time. These insights identify bias, support accreditation and enable program managers and auditors to govern AI confidently.

Our applied research customizes transformers, quantization and edge inference to transform raw mission data into rapid, actionable insights, even on resource-limited devices. We combine automated feature engineering, continual learning, reduced training compute and security to accelerate deployment across enterprise and tactical networks.

AI models are aligned with secure knowledge bases through vector retrieval, prompt optimization and policy filters. Retrieval-augmented generation provides current facts, while guardrails prevent hallucinations and leakage. They support multilingual chat, summarization, entity extraction, reasoning and faster research, reporting and decision-making without compromising confidentiality, integrity or compliance.