Kundi Yao

Kundi Yao is a Research Fellow at the University of Waterloo, Canada, specializing in empirical software engineering with a focus on log management, performance engineering, and AI-driven software development (AI4SE/AIOps). He actively contributes to the academic community as a Program Committee member for AIware 2025, SANER 2026, PROMISE 2025, and other leading software engineering conferences.

His research spans empirical methodologies for software log analysis, mining software repositories, and performance optimization, emphasizing practical applications in AI-enhanced software engineering. Key themes include leveraging log data for system reliability, automating performance testing, and integrating AIOps principles to bridge IT operations and development workflows.

Recent publications like his ICST 2025 work on microbenchmarking demonstrate a trend toward efficient, automated performance validation techniques. This aligns with broader industry movements in continuous integration and AI-augmented software quality assurance.

No scientific awards or honors were documented in the source material. Similarly, details regarding student advising, research grants, or laboratory affiliations remain unreported.