Chih‐Hong Cheng

Papers

Total Citations

246

H-Index

About

Chih-Hong Cheng is a researcher whose work sits at a critical intersection of formal verification, safety assurance, and artificial intelligence. He is best known for his pioneering contributions to the resilience analysis of Artificial Neural Networks (ANNs), particularly in the context of safety-critical applications such as autonomous and self-driving vehicles. His landmark 2017 paper, "Maximum Resilience of Artificial Neural Networks," has garnered over 238 citations, reflecting the profound relevance of his work to the growing field of trustworthy AI. In it, Cheng addresses fundamental verification and certification challenges, examining how ANNs respond to noisy or adversarially perturbed inputs — a concern of paramount importance as machine learning systems are increasingly deployed in high-stakes environments. Beyond neural network verification, Cheng has also contributed to the field of distributed systems design, including work on priority synthesis — a formal method for ensuring safety guarantees in systems of interacting components. Together, his research advances the scientific foundation for building AI systems that are not only capable, but provably safe, making his work essential reading for researchers navigating the frontiers of reliable autonomous technology.