About me
Jia Yu is a co-founder of Wherobots Inc., a Spatial Intelligence Cloud platform for spatial data ETL, analytics, and AI. He was a Tenure-Track Assistant Professor of Computer Science at Washington State University from 2020 to 2023. He obtained his Ph.D. in Computer Science from Arizona State University. His research focuses on large-scale database systems and geospatial data management. In particular, he worked on distributed geospatial data management systems, database indexing, and geospatial data visualization. Jia’s research outcomes have appeared in the most prestigious database / GIS conferences and journals, including SIGMOD, VLDB, ICDE, SIGSPATIAL and VLDB Journal. He is the main contributor of several open-sourced research projects such as Apache Sedona, a cluster computing framework for processing big spatial data, which receives 1 million downloads per month and has users / contributors from major companies.
My work on distributed geospatial database management systems (DBMSs)
I pioneered the field of distributed geospatial data management by creating the first full-fledged in-memory cluster computing framework for large-scale spatial analytics. My work became the industry standard, receiving over 2 million downloads per month and graduating as an Apache Software Foundation top-level project.
- apache/sedona - A cluster computing framework for processing large-scale geospatial data
- wherobots/havasu - The spatial table format for spatial lakehouse
My work on Apache Sedona community tools
- apache/sedona-website - Apache Sedona Website
- jiayuasu/sedona-tools - Tools for Apache Sedona community management
- jiayuasu/geotools-wrapper - GeoTools Wrapper for Apache Sedona
- jiayuasu/sedona-publish-python - Publish Apache Sedona Python packages
- jiayuasu/GeoSparkTemplateProject - Template projects for GeoSpark, GeoSpark-SQL, GeoSpark-Viz
- jiayuasu/sedona-sync-action - Automatically publish Sedona resources periodically
My work on database indexing
I introduced a new class of lightweight and machine learning-enhanced database indexes that dramatically reduce storage overhead while maintaining query performance.
- DataSystemsLab/hippo-postgresql - Hippo, a fast, yet scalable, sparse database indexing approach in PostgreSQL
- microsoft/ALEX - A library for building an in-memory, Adaptive Learned indEX
- DataOceanLab/GLIN - A lightweight learned index for spatial range queries on complex geometries
- jiayuasu/stx-btree - Machine Learning based B+ Tree
- jiayuasu/bitmap-postgresql - An on-disk bitmap index inside PostgreSQL
My work on geospatial data visualization
I developed scalable geospatial visualization techniques for big data, from offloading map rendering to distributed clusters to using smart sampling methods that reduce data-to-visualization time from minutes to sub-second.
- jiayuasu/Tabula - Turbocharging Geospatial Visualization Dashboards via a Materialized Sampling Cube Approach
My previous life
My research lab and my students: https://jiayuasu.github.io/lab/
My publication: https://jiayuasu.github.io/publication/