Andrew Yan
Computer Scientist at U.S. Geological Survey (USGS)
Madison, Wisconsin, United States
Summary
👤
Senior🎓
Top SchoolAndrew Yan is a computer scientist and flexible full-stack developer with 13 years of experience building data-driven web services and automating cloud infrastructure, currently delivering national-scale water data solutions at the U.S. Geological Survey. He combines hands-on Python backend work, Postgres spatial data management, and AWS provisioning with team leadership in agile delivery and stakeholder communication. His MS in Chemistry from UW–Madison underpins a disciplined, experimental approach to problem solving—evident from earlier lab and teaching roles—and informs his attention to data quality and reproducible workflows. An active open-source contributor, he has improved geospatial querying in the widely used pygeoapi project, optimizing PostgreSQL providers and spatial operators to make RESTful OGC APIs more efficient.
13 years of coding experience
3 years of employment as a software developer
Bachelor of Science (BS), Chemistry, Bachelor of Science (BS), Chemistry at University of California, BerkeleyMaster of Science (MS), Chemistry, Master of Science (MS), Chemistry at University of Wisconsin-Madison
Github Skills (7)
api10
postgresql10
geospatial10
sql10
python10
ogc9
pytest8
Programming languages (12)
Github contributions (5)
geopython/pygeoapi
Oct 2019 - Nov 2019
pygeoapi is a Python server implementation of the OGC API suite of standards. The project emerged as part of the next generation OGC API efforts in 2018 and provides the capability for organizations to deploy a RESTful OGC API endpoint using OpenAPI, GeoJSON, and HTML. pygeoapi is open source and released under an MIT license.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 commits, 1 PR, 6 comments in 13 days
Contributions summary:Andrew focused on improving the PostgreSQL provider within the pygeoapi project. Their contributions included implementing and refining bounding box queries, switching spatial operators, and optimizing the code by cutting down excessively long lines. The user also addressed formatting issues by removing numbers in the formatting process and improving the integration of conditions within the WHERE clause. These changes enhanced the functionality and efficiency of geospatial data querying within the project.
pythondataogc-apiendpointorganizations
USGS-CIDA/falcon-hello-world
May 2017 - Jul 2018
Contributions:64 PRs, 51 pushes, 4 branches in 1 year 1 month
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