
Worked on the openanolis/sglang repository to enhance CI reliability and enable scalable inference through Elastic Expert Parallelism. Developed features that migrated test infrastructure to H2O-based disaggregation, integrated new jobs into the workflow, and refined RDMA device selection, addressing dependency conflicts for more stable builds. Introduced an elasticity-aware load balancing algorithm that dynamically manages active ranks, laying the foundation for more flexible and efficient inference workloads. Leveraged Python, YAML, and workflow automation to streamline CI/CD processes and support distributed systems. The work improved feedback cycles and resource efficiency, demonstrating depth in parallelism, dependency management, and automated testing practices.
In 2025-10, sgLang progressed on two major fronts: CI reliability and scalable inference via Elastic Expert Parallelism (EP). The work delivered concrete features, reduced build instability, and positioned the project for larger workloads, with a clear business value in faster feedback and more efficient resource usage.
In 2025-10, sgLang progressed on two major fronts: CI reliability and scalable inference via Elastic Expert Parallelism (EP). The work delivered concrete features, reduced build instability, and positioned the project for larger workloads, with a clear business value in faster feedback and more efficient resource usage.

Overview of all repositories you've contributed to across your timeline