Atef Emran
About Atef Emran
Atef Emran is a Software Engineer specializing in Autonomy Perception Simulation at Torc Robotics, where he has worked since 2022. He has a diverse background in automotive engineering, having held various roles at General Motors, Clemson University, and other organizations, and holds a Master's degree in Automotive Engineering from Clemson University.
Work at Torc Robotics
Currently, Atef Emran serves as a Software Engineer specializing in Autonomy Perception Simulation at Torc Robotics in Stuttgart, Baden-Württemberg, Germany. He has been in this role since 2022. Previously, he worked as a Co-op Software Engineer in Autonomous Simulation at Torc Robotics for seven months in 2022, located in Blacksburg, Virginia, United States. In his positions, he has focused on developing tooling for generating synthetic sensor data and validating sensor models to support autonomous driving software evaluations.
Education and Expertise
Atef Emran holds a Master's degree in Automotive Engineering from Clemson University International Center for Automotive Research, which he completed from 2020 to 2022. He also earned a Bachelor of Science in Mechanical Engineering from Al-Azhar University, studying from 2012 to 2016. Additionally, he achieved a Lean Six Sigma Black Belt certification from the University of Michigan in 2020, demonstrating his expertise in process improvement and quality management.
Background
Atef Emran has a diverse background in engineering, with experience in various roles across the automotive industry. He began his career with internships, including a position as a Turbo-Machinery Design Intern at IST Industries in 2015 and as a Repair and Service summer trainee at Egyptian Automotive & Trading Co. in 2014. His professional journey includes significant roles at General Motors, where he worked in quality engineering and product improvement from 2016 to 2020.
Achievements
Throughout his career, Atef Emran has led the Advanced Product Quality Planning (APQP) process for locally manufactured parts and new models at General Motors. He has utilized data replay from real-world test data to generate metrics for evaluating the autonomous driving stack. His work has included developing tooling for generating synthetic sensor data and validating sensor models, contributing to advancements in autonomous driving technology.