Over 12 years experience building transformative AI systems for critical air, ground, maritime, space, cyber, and commercial domains - from novel research concepts to mission-ready deployment.
Principal AI Research Engineer recognized for solving ambiguous, high-impact technical challenges through the development of novel AI algorithms where conventional approaches are insufficient. My work spans the conception, development, and operational deployment of research across multimodal AI, computer vision, autonomous systems, foundation models, agentic AI, and more. I thrive in ambiguous research environments, applying first-principles thinking to develop practical technologies that extend beyond incremental improvements.
Developing byte-native, long-context foundation models that learn shared semantic representations directly from raw encoded data across text, imagery, audio, video, and arbitrary binary formats.
Key advancement: modality-agnostic semantic learningIntroduced graph neural reasoning into object detection to encode spatial relationships among detections, reduce false alarms, and extend perception toward multimodal fusion and open-vocabulary prediction.
Key advancement: relational reasoning beyond independent detectionsInvestigated neural operators that preserve complex-valued representations and phase information through forward and backward propagation for radar and signal-rich sensing problems.
Key advancement: learning directly in complex-valued domainsDeveloped sequence-prediction approaches for estimating missing high-frequency Fourier components, enabling SAR super-resolution through signal reconstruction rather than conventional pixel interpolation.
Key advancement: predicted signal content instead of interpolating pixelsDeveloped cooperative multi-agent approaches for distributed UAV sensing, resource management, mission planning, and persistent target custody in dynamic and contested environments.
Key advancement: decentralized coordination under changing prioritiesDeveloped perception approaches that recognize uncertainty, reject unknown objects, and expand beyond fixed class taxonomies through multimodal semantic alignment.
Key advancement: recognition beyond predefined classesLead research, architecture, and engineering across emerging AI technologies, including omnimodal language models, grounded AI, semantic search, autonomous systems, large-scale distributed training, and agentic orchestration. Helped scale an early-stage research organization through successive acquisitions while guiding programs from technical concept through operational delivery.
Developed advanced algorithmic and AI approaches for remote sensing platforms using computer vision, graph-based reasoning, multimodal fusion, open-set perception, and more.
Designed and implemented machine-learning systems for perception, predictive analytics, and defense applications, translating experimental models into usable engineering capabilities.
Applied statistical modeling, predictive analytics, and large-scale data analysis to guide executive decision-making and improve business operations through quantitative insight.
B.S., Physics — Kutztown University, 2014
B.S., Mathematics — Kutztown University, 2014
TS/SCI with polygraph
Deep Learning Specialization — Coursera
Reinforcement Learning Specialization — Coursera
Machine Learning Engineering for Production — Coursera
NVIDIA Multi-GPU & Generative AI Training
GTRI SAR Image Formation & Adaptive Arrays
This resume highlights only a small portion of my experience. My portfolio-grounded AI assistant can answer detailed questions about my research, technical decisions, architecture, leadership experience, and project history using my complete portfolio.
