Shiraj Sen, Ph.D.
About Shiraj Sen, Ph.D.
Shiraj Sen, Ph.D., serves as the Principal Engineer for Autonomy at Motional, where he has been employed since 2023. With a robust background in robotics and autonomous systems, he has held various roles in academia and industry, including positions at GE Research and the University of Massachusetts Amherst.
Current Role at Motional
Shiraj Sen serves as Principal Engineer in the Autonomy division at Motional, a role he has held since 2023. In this position, he focuses on leading the development and adoption of requirement-based development and testing practices across the engineering organization. He also mentors engineers to enhance code quality and tooling, contributing to the overall efficiency and effectiveness of the team.
Previous Experience at Motional
Prior to his current role, Shiraj Sen worked at Motional as Principal TPM and Program Lead from 2022 to 2023. During this year in New York, he was involved in various projects that contributed to the company's advancements in autonomous vehicle technology.
Professional Background
Shiraj Sen has a diverse professional background in robotics and autonomous systems. He worked at GE Research for a total of six years in various capacities, including Senior Robotics and Autonomous Systems Scientist and Technical Lead. His earlier experience includes roles as a Postdoctoral Research Associate and Graduate Research Assistant at the University of Massachusetts Amherst, where he focused on robotics and computer science.
Educational Qualifications
Shiraj Sen holds an Integrated M.Sc. in Mathematics and Computing from the Indian Institute of Technology, Kharagpur, which he completed from 1999 to 2004. He further pursued his studies at the University of Massachusetts Amherst, earning a Master of Science in Computer Science and Robotics in 2007, followed by a Ph.D. in the same field in 2012.
Technical Contributions
In his role at Motional, Shiraj Sen has developed analysis tools for evaluating software components of the Autonomy stack. These tools utilize online monitoring, unit and simulation tests, and on-road performance analysis to ensure the reliability and effectiveness of autonomous systems.