Seung Hyun Kong
About Seung Hyun Kong
Seung Hyun Kong is an Associate Professor at KAIST, specializing in fully autonomous driving technologies. He developed two autonomous vehicles, one of which won the 2018 national robot taxi competition in Korea, and has extensive experience in research roles at leading technology companies.
Work at KAIST
Seung Hyun Kong has been serving as an Associate Professor at KAIST since 2010. His role involves conducting research and teaching in the field of autonomous driving technology. He focuses on advancing the capabilities of fully autonomous vehicles, particularly in handling unexpected situations. His work contributes to the development of innovative solutions in the realm of self-driving technology.
Education and Expertise
Seung Hyun Kong holds a Doctor of Philosophy (Ph.D.) in Aeronautics and Astronautics from Stanford University. He also earned a Master of Science degree from the Polytechnic Institute of New York University. His educational background provides a strong foundation for his research in autonomous driving and reinforcement learning methodologies.
Background in Industry
Before his academic career, Seung Hyun Kong gained valuable industry experience. He worked as a Research Engineer at Samsung Electronics from 1997 to 2000. He later held positions at Qualcomm Inc. as a Staff Research Engineer from 2007 to 2009 and at Polaris Wireless Inc. from 2006 to 2007. These roles allowed him to develop practical skills in research and engineering within leading technology companies.
Achievements in Autonomous Vehicles
Seung Hyun Kong has developed two autonomous vehicles, with notable recognition for his work. His first vehicle won the national robot taxi competition in Korea in 2018, earning the President's prize. This achievement highlights his contributions to the field of autonomous driving and showcases his innovative approaches to vehicle technology.
Research Focus and Goals
Seung Hyun Kong's research is centered on fully autonomous driving, specifically utilizing an end-to-end meta-reinforcement learning approach. He aims to enhance the ability of autonomous vehicles to manage unexpected and unusual situations, including internal control system challenges. His work is pivotal in advancing the safety and reliability of self-driving technologies.