• Led the development and enhancement of WherobotsDB and its distributed query engine, as well as WherobotsAI, by designing and implementing new features and APIs to expand geospatial query capabilities.

• Contributed to Apache Sedona by implementing advanced spatial joins, optimizing and refactoring code, and actively participating in community activities to support adoption and collaboration.

• Provided technical leadership and mentorship to team members, driving the successful delivery of complex projects while fostering a culture of collaboration and innovation.

• Focused on improving performance, observability, and reliability, ensuring seamless and efficient data processing at scale.

Guiding strategic direction and project governance for Apache Sedona. Contributing core enhancements to the distributed spatial query engine, extending Apache Spark, Flink, and Snowflake. Implemented scalable kNN join, spatial indexing optimizations, and STAC-based data ingestion. Developed GPU-accelerated query execution and vectorized spatial query processing to improve performance at scale. Created SpatialBench, a comprehensive benchmarking framework for evaluating spatial query engine performance.

Contributed directly to the standardization of spatial data storage within the Parquet file format. This work involved developing critical improvements to geospatial metadata and logical type specifications, which significantly enhance the interoperability and performance of spatial datasets across various analytical engines.

Identified, reported, and troubleshooted and fixed correctness bugs in Spark SQL's aggregate function behavior under Adaptive Query Execution, contributing to the reliability of Spark's distributed query engine.

Developed custom geospatial join and expression functionalities for the in-memory query engine, enhancing its capabilities in the spatial domain.