• Reinforced student knowledge on thermodynamic concepts by tutoring students and helping them with homework.• Organized and lead review sessions for students before exams.

Propulsion System Dynamic Modeling and Controls• Collaborated with senior NASA engineers in developing a simulation for a small turbofan engine to ensure system optimization in preparation for real-time simulations.• Introduced and designed a machine learning PI controller that successfully controlled engine fuel flow by predicting PI gains based on the state of the low-pressure shaft rotational speed; this controller was later tested in a hardware-in-the-loop simulation in preparation for tests on a real turbofan engine.• Achieved less than 6% tracking error in transient response and less than 1% tracking error in steady state response.• Presented simulation test results in front of professional NASA engineers, and recommended future work for the project. • Composed a 9-page project report containing project summary, machine learning approach used, final project results, and insights on the potential of Machine Learning Control in the field of propulsion systems control.

Gamera-S Solar-Powered Quadcopter• Collaborated with aerospace engineering professors to further develop the world’s first solar powered quadcopter. • Performed repairs on current solar panels and constructed new solar panels to optimize power output.• Conducted a data quality comparison test for a LIDAR and ultrasonic altitude sensor to determine which sensors should be implemented onto the vehicle. • Fabricated new vehicle components for the team using a MakerBot appliance and Autodesk Inventor. • Succeeded in assembling the final product and achieved the first successful functionality test of all the vehicle components.

Surrogate Modeling to Improve UAV Design and Path Optimization• Collaborated with mechanical engineering research professors to develop an optimized path planning algorithm for a UAV flying over inhabited areas in a simulation environment.• Identified design parameters for a fixed-wing aircraft that affected its crash location after instantaneous engine failure through rigorous literature review. • Perfected a design of experiments that ran multiple crash simulations of a fixed-wing aircraft with different design inputs (i.e. location of aerodynamic center, gross mass, wingspan, and control surface deflection limits) through a Monte Carlo algorithm on MATLAB.• Designed a Gaussian Process Regression model that predicted a UAV’s crash locations using simulation results as training data; my model adapted to changes in three different design inputs and matched simulation results with 80 percent accuracy. • Optimized research team’s path planning algorithm by incorporating the predictive model to influence UAV risk mitigation. • Composed an 8-page research report containing project summary, approaches, results and potential applications of results in the aerospace industry.