Extensive experience in development and deployment of AI/ML, including but not limited to Natural Language Processing/Conversational (NLP/C) AI Computer Vision and Supervised Learning. NLP/C AI technology deployment has decreased Law Enforcement Officers (LEOs) documentation and filing times by up to 90% allowing for LEOs to stay focused on those who they protect and serve.
Coordinated with multiple University Affiliated Research Centers (UARCs) to deploy several Patterns of Life AI algorithms for a maritime Homeland Defense system along the Great Lakes which has reduced Border Patrol Agents’ reporting times by 800% while also identifying suspicious acting vessels earlier.
Created custom algorithms for of EO/IR imaging and Hyperspectral imaging sensors for automatic detection and identification of targets of interest as well as dust mitigation for MW/LW IR sensors. Algorithms were based on an array of statistical methods and mathematical transforms, such as contrast limited adaptive histogram equalization (CLAHE), spectral and spatial transforms, hyperspectral anomaly detection and target identification, computer vision, and sensor fusion.
Developed and contributed to the library of modules for a patented AI modular framework, OneVision, that streamlines the development testing validation and deployment of advanced data processing algorithms. OneVision provides the key advantage of taking multiple inputs with dissimilar data sources and fusing them into actionable insight in real time for critical decision-making in Homeland Defense.
