Papers
1
Total Citations
3
H-Index
1
About
Ying Hung is a leading researcher at the intersection of robotics, machine learning, and dynamical systems, with a core focus on developing data-efficient methods for understanding and controlling complex robotic behaviors. Her most notable contribution is a groundbreaking framework that integrates surrogate modeling with topological analysis to characterize the global dynamics of robot controllers—including black-box systems—using dramatically less data than traditional approaches. By training Gaussian Processes on randomized short trajectories, her work enables robust, confidence-guaranteed insights into system behavior, a critical advance for real-world deployment where data collection is expensive or risky. This 2023 paper, already garnering 3 citations, exemplifies her talent for bridging theoretical rigor with practical engineering. Hung’s research is particularly impactful for autonomous systems, where understanding global dynamics from sparse data is essential for safety and reliability. Her achievements position her as a rising authority in data-efficient robotics, offering tools that empower other researchers to tackle complex control problems with greater efficiency and confidence.
Research Focus
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Top Papers
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