Since starting full-time at SpaceX in 2021, I've worked on the Dragon and Starship programs primarily as a simulation software engineer. By enabling operators to practice with a physics-accurate representation of these vehicles, my work has allowed mission specialists to perfect their procedures and credibly believe in their safety for the vehicle. I was also responsible for delivering software completion for NASA's Crew-5 mission.

As an intern in 2019 and 2020, I worked on software for both the Dragon spacecraft and Starshield. My work then spanned across build system improvements, state machine design, updates, and debugging, GNC algorithms, and physics/simulation software upgrades.

Under Professor Peter McMahon's lab in Cornell's applied physics department, I'm exploring the limits of the expressive power of quantum neural networks, in comparison to classical neural networks. To be specific, we are asking (and answering) the question, is there a training task for which a quantum neural network provides a theoretically understood and experimentally demonstrable advantage over classical neural networks?

Under Professor Mason Peck in Cornell's mechanical engineering department, I've previously led the execution on two projects: Pathfinder for Autonomous Navigation (PAN) and a control moment gyroscope-based (CMG) rover. The former is a CubeSat flight project expected to launch NET summer 2021; the latter is an early-stage technology demonstration that is still under work in the lab.

Building on expertise gained at SpaceX, my primary role in the lab has been to architect, implement, and manage a team of 10+ software engineers in improving a large codebase for PAN (check it out). We run a lean operation, and are reliant on the immense talent and drive of our small team to manage a codebase as complex as ours.

As one of my first professional software engineering experiences, I was responsible for building out the entire smart contract platform and parts of the fiat on-ramp for CarbonUSD, a multi-blockchain currency produced by Carbon.

M. Eng, Computer Science, College of Computing and Information Science

BS, Applied Physics, College of Engineering