Used large language models (LLMs) and advanced algorithms to accelerate research in education, clean energy, and scientific communication, enabling rapid understanding of unfamiliar domains and informed decision-making.
• Utilized large language models to synthesize technical research and compare solution options for The International Rescue Committee, enabling scalable renewable energy solutions to replace diesel-powered irrigation systems.
• Leveraged large language models to devise a novel ROI calculation framework for Lumina Foundation, identifying scalable data sources to enable continuous tracking of educational credentials across diverse demographics.
• Developed algorithms for representing and transmitting complex scientific concepts through symbolic communication for METI International, with potential applications in AI communication and knowledge systems.

Designed and rapidly prototyped AI-powered systems from concept to implementation.
• Led multimillion-dollar NASA-funded project as Principal Investigator, evolving decision-support interfaces into bi-directional, transparent agent communication models, reducing pilot workload by 29%.
• Directed user experience development program for AI-powered flight systems, establishing workflows through collaboration with UX designers on prototypes deployed as realistic interfaces for iterative user testing and refinement.
• Developed reusable design framework for human-autonomy interaction patterns used across programs to prototype and evaluate AI systems, including advanced flight deck and spacesuit platforms.
• Developed agent that learned a simulated pilot's alerting preferences through reinforcement learning, enabling personalized, context-aware behavior.

Created intelligent agents with human-like reasoning and learning capabilities.
• Designed novel tool that enables human-in-the-loop interface testing and automatically generates predictive operator agents from recorded user interactions for simulation-based performance evaluation.
• Created AI agents with new spatial reasoning capabilities, enabling them to interpret environmental context, navigate within simulated spaces, and trigger procedures dynamically in complex domains such as robotic control.
• Created an agent that learns UAV maneuvering strategies from pilot data, achieving human-level performance in simulation and demonstrating principles for generalizing to physical flight environments.
● Additional Relevant Experience