Electronics and Telecommunications Research Institute (ETRI) - South Korea
Professional Profile
SCOPUSĀ Ā
Early Academic Pursuits
Dr. Jongchan Baek's academic journey commenced at Sungkyunkwan University, where he pursued a Bachelor of Science in Mathematics, graduating with an outstanding GPA of 4.14 out of 4.5. This early academic foundation laid the groundwork for his subsequent pursuit of knowledge in the field of Convergence IT Engineering at Pohang University of Science and Technology (POSTECH), where he earned his Ph.D. His doctoral thesis, supervised by Professor Soohee Han, focused on "Performance-Preserving Sequential Multitask Reinforcement Learning and Its Application to Control Systems," showcasing his early interest in reinforcement learning and control applications.
Professional Endeavors
Dr. Baek's professional journey as an AI researcher at the Electronics and Telecommunications Research Institute (ETRI) in South Korea has been characterized by a profound commitment to advancing the fields of reinforcement learning, embedded applications, robotic control, Robotics and anomaly detection. His involvement in diverse projects, such as the development of advanced air-mobility platforms, DNA+Drone platforms, and drone control technologies, underscores his expertise in applying AI techniques to real-world applications.
Contributions and Research Focus
Throughout his career, Dr. Baek has made significant contributions to the field of AI and robotics through his research endeavors. His publications in reputable journals and conferences demonstrate his expertise in areas such as reinforcement learning for control systems, bridging the simulation-to-real gap, electrochemical-mechanical modeling, and continual learning methods. His research focus on reinforcement learning control algorithms, Robotics particularly in the context of embedded applications and robotic control, has contributed to advancements in autonomous systems and anomaly detection techniques.
Accolades and Recognition
Dr. Baek's outstanding contributions to the field have been recognized through numerous awards and honors. Notable accolades include the 2nd Prize in the NeurIPS 2022 Real Robot Challenge, Best Paper Awards at prestigious conferences like KIEE and ICROS, and recognition for his contributions to various research projects funded by government agencies and industry partners. These accolades underscore his exceptional research acumen and the impact of his work within the scientific community.
Impact and Influence
Dr. Baek's research has had a profound impact on both academia and industry, driving advancements in autonomous systems, robotic control, and anomaly detection technologies. His innovative approaches to reinforcement learning control and embedded applications have contributed to the development of more efficient and adaptive systems capable of Robotics addressing real-world challenges. His collaborations with industry partners and involvement in major projects reflect the practical significance of his research in shaping the future of AI-enabled technologies.
Legacy and Future Contributions
As Dr. Baek continues to pursue his research endeavors, his legacy as an AI researcher committed to advancing the frontiers of knowledge in reinforcement learning, embedded applications, and robotic control is assured. His future contributions are poised to further propel the field forward, with a focus on developing more robust and scalable AI solutions for complex real-world applications. Dr. Baek's dedication to excellence and his passion for pushing the boundaries of AI technology ensure that his impact will be felt for years to come.
NOTABLE PUBLICATIONS
Reinforcement learning to achieve real-time control of triple inverted pendulumĀ Ā 2024(3)
Belief Projection-Based Reinforcement Learning for Environments with Delayed FeedbackĀ 2023(1)
Efficient Multitask Reinforcement Learning Without Performance LossĀ Ā 2023(2)
An Adaptive Model Uncertainty Estimator Using Delayed State-Based Model-Free ControlĀ Ā 2022(12)
Hindsight Intermediate Targets for Mapless Navigation With Deep Reinforcement LearningĀ 2022(4)