Experience

Infinite Orbits SAS Toulouse, France
Computer Vision and GNC Intern June 2022 ~ August 2022
  • Constructed a satellite rendezvous simulator and scene renderer based on Unreal Engine 5 to create images for training and validating neural networks for spaceborne computer vision

Space Rendezvous Laboratory (SLAB), Stanford University Stanford, CA
Research Assistant | Advisor: Dr. Simone D'Amico September 2019 ~ Current
  • Developing robust deep learning models and GNC algorithms for vision-based rendezvous with known and unknown targets in space
  • Developed and calibrated the Testbed for Rendezvous and Optical Navigation (TRON) facility at SLAB that is capable of physically simulating spacecraft proximity operations with a mockup satellite mdodel under high-fidelity spaceborne illumination settings and estimating high-accuracy pose labels
  • Developed the next-generation open-source datasets (e.g., SPEED+, SHIRT) using TRON to train and validate spaceborne vision-based deep learning and GNC algorithms with emphasis on robustness across domain gap
  • Organized the second international Satellite Pose Estimation Competition (SPEC2021) in collaboration with the European Space Agency

Stanford University Stanford, CA
Teaching Assistant | AA279A: Space Mechanics 2019 ~ 2022
  • 3 times in Winter quarters of 2019, 2021, 2022

Harve Mudd College Claremont, CA
Student Researcher | Advisor: Dr. Philip D. Cha May 2016 ~ May 2017
  • Developed a method to accelerate the modal convergence of the eigen-characteristics of uniform and non-uniform rods carrying various lumped attachments

Harve Mudd College Claremont, CA
De Pietro Fellow | Advisor: Dr. Ziyad Duron May 2016 ~ May 2017
  • Developed a method to assess the functionality of steel anchors embedded within a concrete dam based on the Performance-Based Testing using spectral analysis, spectrogram, and model verification
  • Analyzed the earthquake response of Monticello dam by constructing and evaluating a lumped element model of dam, reservoir and a spillway