Rob See

Rob See has a diverse work experience spanning multiple industries. Rob is currently working as a Senior Aerospace Engineer at Trusted Space, Inc. starting from June 2021. Prior to this, they were employed at L3Harris Technologies where they held various roles including Program Manager, Senior Aerospace Engineer, and Aerospace Engineer from 2017 to 2021. Additionally, they served as the Global Optical Network Operations Lead from 2018 to 2019 at the same company. Rob also gained research experience as a Graduate Research Assistant at The University of Texas at Austin, where they focused on optimal orbital insertion trajectories and moon targeting. Rob also worked as a Graduate Teaching Assistant, assisting students in various space-related courses. Rob has hands-on experience as a Systems Engineer at Texas Guadaloop, a student-driven organization participating in SpaceX's Hyperloop Pod Competition. Rob was responsible for leading initial structural design, electronics integration, and testing. Prior to this, they worked as an Undergraduate Research Assistant at The University of Alabama, conducting FEM analysis and developing MATLAB code for aeroelasticity and structural dynamics research. Additionally, they had a summer internship at Teledyne Brown Engineering, where they worked on the Multiple-User System for Earth Sensing (MUSES) project and conducted analysis and verification tasks. Rob's earliest work experience includes a role as a summer intern in logistics at Stevens Transport in 2011.

Rob See received a Bachelor of Science (BS) degree in Aerospace Engineering from The University of Alabama, where they studied from 2010 to 2014. Rob then went on to pursue a Master of Science (MS) degree in Aerospace Engineering from The University of Texas at Austin, completing their studies from 2014 to 2017. In addition to their formal education, Rob has obtained several certifications from Coursera, including "Unsupervised Learning, Recommenders, Reinforcement Learning" in May 2023, "Advanced Learning Algorithms" in April 2023, and "Supervised Machine Learning: Regression and Classification" in January 2023.