Jamil Hammoudeh

B.S. Aerospace Engineering, Astronautics emphasis

• Open-source, headless simulator for testing AI (large language model) and classical controllers on spacecraft rendezvous with keep-out safety constraints.
• Clohessy-Wiltshire relative-motion dynamics validated against the closed-form solution to sub-millimeter accuracy over one orbit; simulates one hour of flight in under 0.1 s.
• Provider-neutral LLM agent interface that logs every prompt, response, parse failure, and latency; safety-filter wrapper that counts interventions.
• First result: a PD baseline violates the keep-out zone in 100% of 30 obstructed approaches, motivating safety-filtered autonomy.
• In progress: a benchmark paper comparing LLM agents, classical controllers, and safety-filtered versions, and a paper on safety-shielded AI for approaching unknown asteroids.

Engineering Project Management & Aircraft Systems Intern. Long Beach, CA and Shannon, Ireland.

• Ireland rotation (May to Aug 2024): Boeing 767-300 structural modification and recovery program under FAA/EASA Part 145 oversight. Supported 15+ licensed engineers installing and aligning six fuselage skin panels to ±0.5 mm.
• Recorded 40+ torque and traceability entries per shift and verified certifications and fit-up clearances for 20+ structural components. The program stayed inspection-ready through QA audits.
• Long Beach: built schedules, part tracking, and inspection logs for 30+ work packages across 5+ repair programs, and coordinated 25+ engineering, maintenance, and supply-chain staff.
• Field projects: supported a Boeing 747 installation at the California Science Center and a Boeing 767 project in San Bernardino.

Leading the design of a registered Boom Prize entry, a sub-25 kg turbojet RC aircraft built to exceed Mach 1. Siemens NX, ANSYS Fluent CFD, ANSYS Mechanical FEA, MATLAB, Python.

20+ ribs, spars, composite molds, and CNC fixtures in SolidWorks for a 1.2 m composite prototype targeting 10 to 15% higher lift-to-drag, held to ±0.3 mm for wind-tunnel testing. 9-person team.

10+ composite layups, CNC-machined molds and airframe panels for an emergency-response VTOL prototype.

A Quran recitation app that uses machine learning on recited audio to detect tajweed errors and give corrective feedback.