< KAIST Professor Insik Shin > KAIST announced on December 21st that Professor Insik Shin from the School of Computing has received the Influential Paper Award 2025 at the IEEE Real-Time Systems Symposium (RTSS), the world's most prestigious international conference in the field of real-time systems. This honor is a "Test of Time Award," presented to papers that have exerted a sustained and significant influence on both academia and industry for more than 10 years after publication.
2025-12-22Kathleen A. Kramer, President of the IEEE (Institute of Electrical and Electronics Engineers), the world's largest technical professional organization dedicated to electrical and electronic technology, visited our university on the 30th and delivered a special lecture under the theme, 'Drawing the Future of Artificial Intelligence Together.' < IEEE Leadership and KAIST EE Meeting KITIS Director (Sung-Hyun Hong), KAIST EE Professors (Joonwoo Bae), (Ian Oakley), (Hye-Won Jeong), (Chang-Shik
2025-11-06- Professor Jee-Hwan Ryu of Civil and Environmental Engineering receives the Best Paper Award from the Institute of Electrical and Electronics Engineers (IEEE) Robotics Journal, officially presented at ICRA, a world-renowned robotics conference. - This is the highest level of international recognition, awarded to only the top 5 papers out of approximately 1,500 published in 2024. - Securing a new working channel technology for soft growing robots expands the practicality and application possib
2025-06-09< Photo 1. (From left) Daebeom Kim (Team Leader, Ph.D. student), Seungjae Lee (Ph.D. student), Seoyeon Jang (Ph.D. student), Jei Kong (Master's student), Professor Hyun Myung > A team of the Urban Robotics Lab, led by Professor Hyun Myung from the KAIST School of Electrical Engineering, achieved a remarkable first-place overall victory in the Nothing Stands Still Challenge (NSS Challenge) 2025, held at the 2025 IEEE International Conference on Robotics and Automation (ICRA), the world
2025-05-30- The team led by Professor Hyun Myung of the School of Electrical Engineering developed “DreamWaQ”, a deep reinforcement learning-based walking robot control technology that can walk in an atypical environment without visual and/or tactile information - Utilization of “DreamWaQ” technology can enable mass production of various types of “DreamWaQers” - Expected to be used in exploration of atypical environment involving unique circumstances such as disasters
2023-05-18