Papers
3
Total Citations
10
H-Index
2
About
Xuanliang Deng is a researcher at the forefront of safety-critical autonomous systems, with a focus on the intersection of real-time scheduling, control theory, and robotics. His major contributions lie in developing formal frameworks that enable autonomous robots to reason about and trade off safety and performance under computational constraints. In his highly cited 2023 work, Deng introduced a novel Safety-Performance (SP) metric, a first-of-its-kind analysis that allows robots to quantitatively balance safety and task-critical computational tasks, a foundational step for computational awareness in autonomous systems. He extended this in 2024 with a partitioned scheduling approach for stochastic conditional DAG models, providing a rigorous method for managing safety-performance trade-offs in complex, uncertain environments. Beyond safety analysis, Deng has demonstrated creative technical breadth in his 2020 work on robot calligraphy, where he employed pseudospectral optimal control and a novel dynamic brush model to transform the artistic challenge of Chinese calligraphy into a solvable trajectory optimization problem. With his papers already garnering early citations, Deng is establishing himself as a key voice in making autonomous systems both safer and more capable.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3