Yifan Wu

Yifan Wu is a Postdoctoral Researcher at the School of Computer Science, Peking University. With a Ph.D. in Software Engineering from Peking University (2019-2024), Wu focuses on applying artificial intelligence techniques to software engineering problems, particularly in the domain of AIOps.

Wu's educational background includes:

• Ph.D. in Software Engineering, Peking University (2019-2024), supervised by Prof. Ying Li
• B.E. in Software Engineering, University of Electronic Science and Technology of China (2015-2019)

Research interests center around AIOps, with particular emphasis on log analysis, anomaly detection, and root cause analysis. Wu's work bridges the gap between artificial intelligence and practical software engineering challenges, developing novel approaches for log-based fault diagnosis, software reliability maintenance, and incident management. Recent research has increasingly incorporated large language models for various software engineering tasks, demonstrating innovative applications of cutting-edge AI techniques to longstanding software engineering problems.

Analysis of Wu's recent publications reveals a strong focus on log analysis techniques, particularly log parsing and fault diagnosis. There's a clear trend toward integrating large language models with traditional software engineering practices, as well as a growing interest in change risk detection and incident management. Wu's work spans both theoretical advancements and practical applications in real-world software systems, with numerous publications in top-tier conferences including ASE, ICSE, and ESEC/FSE.

Scientific contributions include:

• Multiple publications in top-tier software engineering conferences (ASE, ICSE, ESEC/FSE)
• Research on log analysis, anomaly detection, and root cause localization
• Studies on change-induced incidents and software reliability
• Applications of large language models to software engineering tasks
• Development of novel frameworks like CSLParser and OCRCL

Wu actively contributes to the academic community as a committee member for conferences including ICSE 2026, ACL ARR 2025, ISSRE 2025, ISSTA 2025, and as a journal reviewer for TOSEM, TSE, and TCC. This demonstrates strong engagement with the research community and recognition of expertise in the field. Current research directions focus on leveraging advancements in artificial intelligence, particularly large language models, to address longstanding challenges in software engineering and operations, with potential applications across various domains of software development and maintenance.