
Worked on IBM/unitxt and oap-project/velox, focusing on data engineering and distributed systems challenges. Developed a configurable confidence interval method for metrics in IBM/unitxt, introducing a ci_method attribute that allows users to select statistical approaches for confidence interval calculations. This enhancement, implemented in Python with statistical modeling techniques, improved the flexibility and accuracy of metric reporting across dashboards. In oap-project/velox, addressed reliability in the Parquet read path for cuDF Presto integration by fixing the reader projection logic in C++. This ensured all necessary columns are read for robust filter pushdown, enhancing query correctness and system stability for analytics workloads.
June 2025 monthly summary for oap-project/velox: Focused on reliability improvements in Parquet read path for cuDF Presto integration. Implemented a Parquet reader projection fix to include all columns referenced by subfields and remaining filters, preventing filter failures when required columns are not explicitly read. This enhances query correctness and stability for cuDF Presto workloads, supporting faster analytics and reducing downtime for deployments.
June 2025 monthly summary for oap-project/velox: Focused on reliability improvements in Parquet read path for cuDF Presto integration. Implemented a Parquet reader projection fix to include all columns referenced by subfields and remaining filters, preventing filter failures when required columns are not explicitly read. This enhances query correctness and stability for cuDF Presto workloads, supporting faster analytics and reducing downtime for deployments.
April 2025 monthly summary for IBM/unitxt focused on delivering a configurable confidence interval method for metrics, enabling more flexible and accurate metric reporting. The work reduces ambiguity in KPI interpretation and supports data-driven decision making across dashboards and reports.
April 2025 monthly summary for IBM/unitxt focused on delivering a configurable confidence interval method for metrics, enabling more flexible and accurate metric reporting. The work reduces ambiguity in KPI interpretation and supports data-driven decision making across dashboards and reports.

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