Yuchi Tian

Papers

Total Citations

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About

Yuchi Tian is a leading researcher at the intersection of software engineering and artificial intelligence, pioneering methods to bring engineering rigor to deep learning systems. His seminal work, "Bringing Engineering Rigor to Deep Learning" (2019), addresses the critical challenge of validating deep neural networks in safety- and security-critical domains such as autonomous driving, robotics, and malware detection. Tian's research focuses on developing systematic testing and verification techniques to ensure the correctness and predictability of DL systems on corner-case inputs—a problem of growing importance as AI permeates real-world applications. With 4 citations, this foundational paper has laid the groundwork for a new subfield of AI reliability, influencing subsequent work on adversarial robustness and formal verification. Tian's contributions are particularly notable for bridging the gap between traditional software engineering practices and the unique challenges of deep learning, offering practical frameworks that enable engineers to detect and mitigate unpredictable behaviors before deployment. His work continues to shape how researchers and practitioners approach the validation of AI systems, making deep learning safer and more trustworthy for high-stakes environments.