Autonomous System Testing: Designed and implemented search-based test generation algorithms to automatically produce high-value test cases, improving scenario coverage and fault detection rates for autonomous driving systems.
LLM-Driven Test Generation: Architected an LLM-driven framework to automatically generate complex, realistic driving scenarios from textual descriptions, significantly reducing manual test design effort.
Explainable AI: Built an interpretability framework to explain generated test cases and autonomous system behaviors, facilitating debugging workflows for black-box autonomous systems.
Industrial-Grade Implementation: Engineered testing pipelines compatible with industry-grade simulators and production-level autonomous driving stacks, collaborating with industry partners to ensure research aligns with deployment needs.
R&D Engineer Intern
Meituan | System Monitoring Team
Log Valuation System: Spearheaded the implementation of a log data valuation system using deep learning and explainable AI to quantify the diagnostic value of log messages for system anomaly analysis.
Large-Scale Validation: Built an end-to-end ML prototype validated on millions of log entries (internal production data and open-source benchmarks), demonstrating potential for reducing storage costs and analysis overhead without compromising observability.
Full-Stack ML: Managed the full lifecycle from data processing and model design to implementation and evaluation.
R&D Engineer Intern
Meituan | System Monitoring Team
RCA Algorithm Deployment: Spearheaded the development of a multi-source anomaly RCA system that integrates time series analysis of metrics, logs, and traces from multiple production services, successfully deploying it to a production environment.
AIOps Capability Building: Established the team’s RCA capabilities from scratch, including system architecture, algorithm selection, and time series analysis techniques for anomaly detection and causal inference.
System Reliability: Collaborated with SREs to ensure system reliability, scalability, and integration with existing monitoring and observability pipelines.
Software Engineer Intern
Transwarp Technology Co., Ltd. | Infrastructure Department
AIOps Platform Dev: Developed core components of an AIOps platform, including log analysis modules, JStack analysis tools, and an operational knowledge base.
Database Internals: Implemented schema evolution features (DDL support) for a distributed columnar database, contributing to large-scale data processing capabilities.
DevOps Engineer Intern
Shanghai Kaian Technology Co., Ltd.
CI/CD Automation: Maintained and enhanced automated build/test/deploy pipelines, improving developer productivity and release reliability.
Education
Ph.D. in Software Engineering
Nanjing University
B.Eng. in Software Engineering
Nanjing University
Services
Program Committee
41st IEEE/ACM International Conference on Automated Software Engineering (ASE) ∙
January 2026
Reviewer
IEEE Transactions on Software Engineering (TSE) ∙
January 2026
Reviewer
IEEE Transactions on Software Engineering (TSE) ∙
July 2025