Jihye Bae

University of Florida, University of Kentucky

Papers

Total Citations

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About

Jihye Bae is a leading researcher at the intersection of reinforcement learning, neural decoding, and autonomous robotics. Her work focuses on developing intelligent algorithms that enable machines to learn from data and interact with the physical world. In her highly cited 2015 paper, Bae introduced the Kernel Temporal Differences (KTD)(λ) algorithm for neural decoding, demonstrating how kernel-based learning can be applied online to decode neural signals—a foundational contribution that has garnered 12 citations and advanced brain-machine interface research. More recently, her 2021 study on the impact of gravity compensation in reinforcement learning for robotic manipulators (10 citations) addresses a critical challenge in autonomous systems: reducing the time and manual modeling required for robots to learn goal-reaching tasks. By integrating classical control insights with modern machine learning, Bae’s work bridges theory and practice, offering scalable solutions for real-world robotics. Her research has been instrumental in making autonomous systems more efficient and adaptive, with applications ranging from prosthetics to industrial automation. Bae’s contributions continue to inspire students and researchers exploring the frontiers of learning-based control and neural engineering.