Washington DC-Baltimore Area
- Using constellation optimization algorithms and nonlinear optimal control to develop an orbital laser swarm solution to orbital debris for collision avoidance of large derelicts - Adapting the CAPO (Control Actuator Placement Optimization) algorithm for use in constellation architecture optimization and developing system dynamics for laser ablative maneuvering of debris objects - Built an in-house high-fidelity orbital dynamics simulator to model debris orbits and subsequent laser ablative propulsion dynamics and validated it against NASA’s GMAT Simulator
-Developed reinforcement learning capabilities for Battle Management Systems for base defense -Designed and tested a scalable multi-agent reinforcement learning solution to air defense using partitioning, cascading decision frameworks, and optimization algorithms -Worked with Leidos’s battle management simulation. Developed sim features, AI capabilities, and testing infrastructure while continuously integrating with existing software stack interfaces -Demonstrated 100% effectiveness improvement over previous agent network
-Designed and executed an optimization study for a low earth orbit (LEO) swarm of space vehicles designed to use laser ablation techniques to perform Just in time Collision Avoidance (JCA) to prevent large on orbit derelicts from colliding with spacecraft. -Developed a 3 DOF orbital simulator in Julia to model a spacecraft swarm of laser platforms and debris cluster and designed a gradient descent optimization algorithm to maximize laser coverage of the derelict cluster by a collaborate laser satellite swarm -Worked a Northrop internal research and development (IRAD) project developing spacecraft buses for efficient LEO constellation deployment via modular payload hosting -Performed Monte Carlo GNC simulations of spacecraft dynamics to validate tipoff stabilization using new sensor suite CONOPS and new satellite simulator features
-Developed an autonomous drone system designed for reinforcement learning powered autonomy to autonomously map an arena, evade an adversary, and inspect/identify points of interest -Lead the autonomy and simulation subteam in the development of the autonomy stack, computer vision algorithms, 3 DOF and 6 DOF simulations for reinforcement learning training, and designing autonomous system architecture -Developed 6 DOF simulation, LiDAR ingestion algorithms, object detection algorithms, and our autonomous ROS control functionalities
-Worked on spacecraft buses for efficient LEO constellation deployment as part of a Northrop internal research and development project (IRAD) -Prepared for program’s preliminary design review (PDR) by performing simulation trials in STK -Analyzed steering modes for the spacecraft’s flight dynamics using STK and Matlab scripting -Managed electrical and logical interfaces between spacecraft components using SysML -Designed, researched, led, and presented solutions to space debris for an internally funded Northrop hackathon.