Education & Professional Experience
Education
- MS, Computer Science | Stanford University | 2022-2023
- BS, Engineering-Physics | Stanford University | 2018 - 2022
Professional Experience
- Research Assistant | Stanford, CA | Stanford Doerr School of Sustainability | Jan 2025 - Present
- Collaborated with Prof. Eric Dunham to enhance a fluid dynamics library, improving accuracy in modeling Vulcanian eruptions.
- Utilized Python, NumPy, SciPy, Matplotlib, and Jupyter Notebooks for data analysis and visualization.
- Software Engineer | Seattle, WA | SpaceX | Aug 2023 - Dec 2024
- Worked on multiple projects related to increasing the software update speed on satellites and user terminals, developed persistent storage safety check, and led telemetry reduction effort.
- Developed and tested C++ and Python vehicle-side software in Hardware-in-the-Loop (HITL) environments.
- Leveraged Docker, Kubernetes, Git, shell scripts, gRPC, and CI/CD pipelines to streamline development processes.
- Software Intern | Seattle, WA | SpaceX | June 2022 - Sept 2022
- Wrote a new ground service in C++ and Python as part of the Fleet Software Team to query Swarm satellite ephemerides and improve ground-to-satellite communication reliability.
- Modeling and Simulation Intern | Colorado Springs, CO | Boecore | June 2021 - Sept 2021
- Worked on a C++ application to facilitate communication between infrared and radar sensors and the Command and Control, Battle Management, and Communications (C2BCM) system.
- Obtained a security clearance.
- Physics/Math Tutor | Stanford, CA | Schwab Learning Center | Sept 2019 - Aug 2021
- Tutored undergrads in mechanics, electricity and magnetism, thermodynamics, algorithms, linear algebra, probability, calculus, and differential equations.
- Software Intern | Durango, CO | Stoneage Waterblast Tools | Dec 2020 - March 2021
- Contributed to full-stack development using Node.js and React; developed IoT solutions for a proprietary robot using AWS IoT Core.
- LSST Physics Research Fellow | Stanford, CA | Stanford Physics Department | June 2020 - Sept 2020
- Used a recurrent neural network in PyTorch to emulate the Large Synoptic Survey Telescope (LSST) deblending pipeline in order to better understand systemic bias in weak lensing probes as a result of unrecognized galaxy-galaxy blends.
