Xiaozhe Gu
Papers
1
Total Citations
28
H-Index
1
About
Xiaozhe Gu is a leading researcher in the safety and reliability of Cyber-Physical Systems (CPS), with a particular focus on the integration of machine learning into safety-critical domains such as autonomous vehicles, robotics, and chemical plants. His most-cited work, "Towards safe machine learning for CPS" (2019, 28 citations), addresses the critical challenge of ensuring that ML-driven decision-making and control systems operate without catastrophic failure. Gu’s contributions lie in developing formal verification and risk-assessment frameworks that bridge the gap between traditional control theory and modern AI, enabling safer deployment of learning-enabled components. His research has been instrumental in identifying and mitigating the unique failure modes introduced by ML in CPS, such as distribution shift and adversarial vulnerabilities. Through his work, Gu has helped shape the conversation around trustworthy AI in real-world, high-stakes environments, earning recognition for advancing both theoretical foundations and practical safety guarantees. His ongoing efforts continue to influence how engineers and researchers design resilient, verifiable autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Towards safe machine learning for CPS28 citations · 2019