Kayhan Space
Vishal Ray has worked in a variety of roles in the fields of astrodynamics and spaceflight mechanics. In 2022, they began working as a Sr. Astrodynamics Engineer at Kayhan Space. In 2021, they were a Visiting Researcher at the NASA Jet Propulsion Laboratory. In 2018, they were an Engineer Intern at SpaceNav, where they validated improved drag coefficient models via simulations based on actual satellites. In 2017, they were a Postdoctoral Researcher and a Graduate Research Assistant at the University of Colorado Boulder, where their project concerned the modeling of atmospheric drag effects on satellites. In 2015, they were a Summer Research Intern at University College London, and in 2014, they were an Engineer Intern at the Indian Space Research Organization (ISRO). In 2013, they were an ADCS team member at Pratham, IIT Bombay Student Satellite, where they were involved in carrying out the Failure Mode and Effect Analysis (FMEA) of the sensors and actuators onboard Pratham and an integrated bench testing of the control law in an emulated space environment.
Vishal Ray has an extensive education history. Vishal obtained a Bachelor's and Master's degrees from Indian Institute of Technology, Bombay in Aerospace, Aeronautical and Astronautical Engineering from 2012 to 2017. Vishal then pursued a Master of Science degree from the University of Colorado Boulder in Aerospace Engineering Sciences from 2017 to 2020 and obtained a Master of Science certification in December 2020. Subsequently, they obtained a Doctor of Philosophy degree from the University of Colorado Boulder in Astrodynamics from 2017 to 2021 and obtained a Doctor of Philosophy certification in December 2021.
Kayhan Space
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For operators of small or large constellations of satellites who need to have a complete situational awareness of their fleet with utmost reliability, Kayhan provides a fully scalable cloud-based collision avoidance service. Unlike other services, Kayhan’s notification system easily integrates with any form of notification channel and ensures that the operators are aware of the upcoming high-interest events. Kayhan uses complex mathematical algorithms and advanced machine learning techniques to aid operators to make the safest avoidance decisions, because at Kayhan we understand that a safe space domain is the best space to operate in