Melbourne, Florida, United States
- Resolve employee concerns and manage daily operations of over 50 employees across 6 learning spaces. - Coordinate with technical and supervisor team to improve efficiency and quality of user support. - Manage reservations of 20 academic technology spaces each semester.
- Superintended designated Academic Technology learning spaces and computer labs on the UF campus. - Troubleshooted issues with lab computers, printers, answer questions about the functionality of Microsoft and Google applications such as Excel, Docs, Word, and PowerPoint.-
- Designed testing skid and setup for 1000N engine with a load cell, pressure transducers, and thermocouples. - Developed written test plan and procedure to facilitate data collection and ensure employee safety. - Created interface control documents for integration of a turbocompressor into the body and ducting of a drone. - Evaluated potential risks for turbocompressor design in a risk matrix and tracked burndown efforts.
- Manufacture fuselage and wings of competition aircraft by laser cutting ribs, negative molding carbon fiber, and epoxying alongside a team of 20. - Design wing assembly components in SolidWorks to optimize aerodynamic performance and minimize weight. - Write technical report sections for competition paper to describe design process, simulations, and testing procedures.
- Promoted growth in leadership qualities among the top 0.5% Herbert Wertheim College of Engineering by discussing various styles, approaches, and perspectives on engineering leadership. - Organized a series of alumni guest speakers and coordinated with a network of over 50 active alumni to foster communication with our current membership.
- Code reinforcement learning simulations in Python using RLlib to autonomously guide an agent to a target. - Develop numerical methods to smoothly guide an agent toward a line-of-sight cone using velocity and position data. - Conduct background research into the Autonomous Rendezvous, Proximity Operations and Docking (ARPOD) problem and learn fundamentals of model predictive control and deep reinforcement learning algorithms.