Xiaobing Dai
Papers
1
Total Citations
7
H-Index
1
About
Xiaobing Dai is an emerging researcher specializing in safety-critical control systems, with a particular focus on the intersection of machine learning and control theory. His work addresses one of the most pressing challenges in modern control engineering: ensuring the safety of autonomous systems operating under uncertainty and unknown dynamics. His most notable contribution, "Learning-Based Prescribed-Time Safety for Control of Unknown Systems With Control Barrier Functions" (2024), introduces a Gaussian process-based framework that combines Control Barrier Functions with data-driven learning techniques to guarantee state constraint satisfaction within prescribed time horizons — a problem that had remained largely unsolved for systems with unknown dynamics. This work has already garnered 7 citations since its publication, reflecting strong early interest from the robotics, autonomous systems, and control communities. Dai's research sits at the critical boundary between theoretical control design and practical machine learning applications, making it highly relevant to real-world deployment of autonomous vehicles, robotic systems, and other safety-critical technologies. His contributions represent meaningful advances in making intelligent systems provably safe even when their underlying dynamics are not fully understood.
Research Focus
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Top Papers
- 1