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New York, NY – September 25, 2026 – Yuan Yingqiang, a researcher specializing in cloud-native and AI-driven distributed systems, has a comprehensive computer science education background, with a bachelor’s degree in Computer Science from Washington State University and a master’s degree from Santa Clara University. With extensive experience in the North American technology industry, he has focused on fault diagnosis and self-healing in cloud-native distributed systems, combining cross-border intellectual property development with research presented at international conferences to continuously develop practical, implementation-oriented engineering approaches.

During his academic studies, Yuan focused on the evolution of distributed data pipelines and cloud-native platform technologies. After graduation, he established his career in the North American technology industry, participating extensively in multiple commercial projects built on AWS cloud infrastructure. His work has covered the development of enterprise data lakes for financial institutions, large language model evaluation systems, the implementation of AI agents for risk management, and large-scale unified batch and stream data pipelines. These projects provided him with extensive hands-on experience deploying and operating large-scale distributed systems in production environments overseas.

Building on this engineering experience, Yuan independently developed two proprietary technical tools and subsequently obtained software copyright registrations from both the National Copyright Administration of China and the United States Copyright Office (USCO), establishing cross-border intellectual property protection. The two tools include Backend Development Data Collection Software V1.0, which focuses on unified collection and normalization of multi-source data at the infrastructure level, and Cloud-Native-Driven Intelligent Application System V1.0, which provides an end-to-end cloud-native development workbench. Both tools have entered pilot applications within enterprises, providing lightweight platform engineering capabilities to technology teams in China and overseas.

On the research front, Yuan’s paper, Self‑Healing and Intelligent Root Cause Analysis System for Data Link Faults in Cloud‑Native Environments, was accepted for the 6th International Conference on Applied Mathematical Modeling and Intelligent Computing (CAMMIC 2026) and included in the ACM conference proceedings. The research addresses persistent industry challenges in microservice architectures, particularly the difficulty of diagnosing data pipeline failures and the slow recovery of distributed systems. The proposed solution integrates non-intrusive eBPF-based observability and data collection, knowledge graphs and graph neural networks for root cause localization, and deep reinforcement learning for automated self-healing decisions. Experiments conducted on a 1,000-node cluster demonstrated that the system could reduce root cause localization to the order of seconds, while significantly improving self-healing success rates compared with conventional static strategies. At the same time, the system maintains controlled operational overhead while preserving fault analysis accuracy, providing a practical technical reference for intelligent operations and maintenance of large-scale cloud-native clusters.

“Fault management in distributed systems cannot rely solely on manual investigation and static threshold-based rules. Kernel-level observability, graph reasoning, reinforcement learning, and other technologies need to be integrated to build practical automated operations and maintenance capabilities,” Yuan Yingqiang said. He hopes to combine frontline engineering practices in North America with research exploration to develop broadly applicable technical solutions that can help enterprises improve the resilience of their cloud-native systems.

Looking ahead, Yuan will continue to focus his research on two key areas: AI-enhanced data pipelines and intelligent fault self-healing for cloud-native systems. He will further iterate on his proprietary tools while strengthening the pathway for translating research outcomes into practical industry applications.

About Yuan Yingqiang

Yuan Yingqiang is a researcher and engineer specializing in cloud-native infrastructure, distributed systems, and AI-driven fault diagnosis and self-healing technologies. With academic training in computer science and industry experience in North America, his work spans enterprise data infrastructure, large-scale distributed systems, and intelligent operations and maintenance. His research and independently developed technical tools focus on improving the resilience, observability, and automation of modern cloud-native systems. His broader goal is to bridge applied research with practical engineering solutions that can be deployed in real-world enterprise environments.

(Written by Jessie Epstein)

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