Suriya Narayanan Lakshmanan
About Suriya Narayanan Lakshmanan
Suriya Narayanan Lakshmanan is a Software Engineer specializing in perception, currently working at Cyngn in Menlo Park, United States. He holds a Master's degree in Computer Vision from Carnegie Mellon University and has experience in developing advanced detection systems and implementing software synchronization for autonomous technologies.
Work at Cyngn
Suriya Narayanan Lakshmanan has been employed at Cyngn as a Software Engineer specializing in Perception since 2019. In this role, he has contributed to several key projects, including the prototyping of stereo-based obstacle detection systems and the development of a traffic light detection and color recognition system from scratch. He also created a 2D object detection system with custom classes and implemented Lidar-Camera software synchronization as part of the autonomous stack.
Education and Expertise
Suriya Narayanan Lakshmanan holds a Master's degree in Computer Vision from Carnegie Mellon University, where he studied from 2017 to 2018. He also earned a Bachelor's degree in Electrical and Electronics Engineering from the National Institute of Technology, Tiruchirappalli (NITT), completing his studies from 2010 to 2014. His academic background is complemented by expertise in deep reinforcement learning and geometry-based techniques for applications such as panorama creation, augmented reality, tracking, and SLAM.
Background
Before joining Cyngn, Suriya Narayanan Lakshmanan worked as a Software Engineer at Texas Instruments in Bangalore, India, from 2014 to 2017. This experience provided him with a solid foundation in software engineering, which he has built upon in his subsequent roles. He has been a graduate student at Carnegie Mellon University since 2017, further enhancing his knowledge and skills in the field.
Achievements
During his time at Cyngn, Suriya Narayanan Lakshmanan has successfully prototyped various systems that contribute to the advancement of autonomous technologies. His work on stereo-based obstacle detection and traffic light detection systems showcases his ability to develop innovative solutions in the field of perception. Additionally, his implementation of Lidar-Camera software synchronization highlights his technical skills and understanding of complex systems.