Gil Shohet
About Gil Shohet
Gil Shohet is a Controls and Simulation Software Engineer II at Astranis, with a strong academic background in Aerospace Engineering from Stanford University. He has extensive experience in high-performance computing, scientific Python, and physical simulation methods.
Work at Astranis
Currently, Gil Shohet serves as a Controls and Simulation Software Engineer II at Astranis, a position he has held since 2021. His role involves the development and implementation of control systems and simulation software, contributing to the company's efforts in satellite technology. Astranis is known for its innovative approach to providing internet access via small satellites, and Shohet's expertise in simulation and controls plays a crucial role in advancing these technologies.
Education and Expertise
Gil Shohet has an extensive educational background in Aerospace, Aeronautical, and Astronautical Engineering. He earned his Doctor of Philosophy (PhD) from Stanford University from 2017 to 2021, where he developed novel tools to study charge attachment to impact debris and its effects on plasma evolution. Prior to his PhD, he completed a Master of Science (MS) at Stanford University from 2015 to 2017 and a Bachelor of Science (BS) at the University of Illinois Urbana-Champaign from 2011 to 2015. His education has equipped him with a broad knowledge of physical simulation methods for fluids and plasmas.
Research Experience
Gil Shohet has gained significant research experience through various roles. He worked as a Graduate Research Fellow at Sandia National Laboratories for three months in 2018 and as a Graduate Student Researcher at Lawrence Livermore National Laboratory and Los Alamos National Laboratory in 2017 and 2016, respectively. His research has focused on high-performance computing for complex simulations, utilizing scientific Python for modeling, data analysis, and visualization.
Technical Skills
Shohet possesses strong technical skills in scientific Python, utilizing libraries such as NumPy, SciPy, Matplotlib, Pandas, and Seaborn for modeling, simulation, data analysis, signal processing, and visualization. His experience includes using high-performance computing resources to conduct complex simulations, as well as employing creative data visualization techniques to extract insights from intricate datasets. This skill set is essential for his current work in controls and simulation at Astranis.