Xuefei Ning
Papers
2
Total Citations
15
H-Index
2
About
Xuefei Ning is a leading researcher at the forefront of reliable and fault-tolerant deep learning systems. Her work addresses a critical challenge in modern AI: ensuring the robustness of neural networks deployed in safety-critical domains such as autonomous driving and robotics. Ning’s major contribution is a hierarchical perspective on fault tolerance, systematically analyzing vulnerabilities from the hardware level up through software and algorithmic layers. Her most-cited paper, the 2022 "Special Session: Fault-Tolerant Deep Learning: A Hierarchical Perspective" (12 citations), and its companion article (3 citations) have become foundational references for researchers seeking to build dependable AI systems. By bridging the gap between theoretical reliability and practical deployment, Ning’s research directly impacts the safe integration of deep learning into real-world applications where failure is not an option. Her work is essential reading for students and engineers aiming to design AI that is not only powerful but also trustworthy under adverse conditions.
Research Focus
Key Achievements
Top Papers
- 1Special Session: Fault-Tolerant Deep Learning: A Hierarchical Perspective12 citations · 2022
- 2Fault-Tolerant Deep Learning: A Hierarchical Perspective3 citations · 2022