Max Doppelt

Developed a generative AI health analytics engine achieving ~85% accuracy on domain-specific queries and delivered a production-ready MVP within 6 months with secure containerized FastAPI microservices and responsive React front end.

Built a generative AI health analytics engine and production-ready MVP within 6 months, achieving ~85% accuracy on domain-specific queries and deploying secure containerized FastAPI microservices for model serving.

Led a 10+ engineer team to deliver the Spectrum Exchange MVP, cut spectrum allocation latency by 50%, and coordinated a successful pilot with U.S. Army stakeholders for operational deployment.

Engineered an AI-driven dynamic spectrum allocation engine achieving 3.5× higher spectrum utilization and maintained >99% link availability via real-time interference detection and automated reallocation.

Spearheaded development of an AI real-time risk scoring engine that improved fraudulent merchant detection precision by 30% and scaled the platform to process 5M+ transactions/day with 99.9% uptime.

Developed ML models that identified high-risk merchants with 92% precision and built a data pipeline processing 100M+ records weekly, reducing batch time by 60%.

Delivered real-time 6-DoF tracking at 60 Hz for non-rigid origami robots, reducing pose estimation error by 15% and validating tracking accuracy under 5 mm in dynamic tests.

Implemented a Selenium-based automated web testing framework and integrated it into Jenkins CI, reducing manual QA regression workload by 40%.

Grade: Magna Cum Laude

Completed a Master of Science in Robotics with a thesis validating a real-time tracking system and reproducible ROS-based deployments.

Completed a Bachelor of Science in Systems Engineering with coursework in optimization, stochastic modeling, and machine learning.