Kanghua Mo
Papers
1
Total Citations
22
H-Index
1
About
Kanghua Mo is a prominent researcher at the intersection of artificial intelligence and cybersecurity, with a primary focus on deep reinforcement learning (DRL) and its security implications. His landmark work, "Security and Privacy Issues in Deep Reinforcement Learning: Threats and Countermeasures" (2024), has rapidly garnered 22 citations, establishing him as a leading voice on the vulnerabilities inherent in AI-driven decision-making systems. Mo systematically maps the threat landscape for DRL agents—from adversarial attacks that manipulate learned policies to privacy leaks in training data—and proposes robust countermeasures that balance performance with safety. This contribution is especially critical as DRL is deployed in high-stakes domains like autonomous driving, robotic control, and quantitative finance. Beyond this seminal paper, Mo’s research continues to explore how to make AI systems both powerful and trustworthy, addressing the pressing need for secure machine learning in real-world applications. His work not only advances theoretical understanding but also provides practical guidelines for developers and policymakers, making him a key figure in shaping the future of resilient AI.
Research Focus
Key Achievements
Top Papers
- 1