Summary
Aaryn Olsson is a software engineer and data scientist with nearly three decades of programming experience and over a decade applying scalable data engineering and machine learning to remote sensing and national intelligence problems. Currently at Planet and teaching Open Source GIS at the University of Arizona, he bridges production systems (Hadoop, Elasticsearch, Kafka, HBase/Accumulo) with spatial databases and geospatial tooling like PostGIS, GeoServer, and GeoMesa. His background spans end-to-end work from low-level C/Unix tooling and custom raster readers to cloud-native devops (Kubernetes, Terraform, Jenkins) and horizontal scaling for geospatial services. Aaryn pairs rigorous academic training (MS and PhD in spatial analysis/remote sensing) with hands-on ecology fieldwork and creative algorithmic approaches—such as treating image patches with random forests as a "poor man's" CNN and applying circuit-theory ideas to landscape networks. He is motivated by large heterogeneous data and enjoys moving research workflows into production, currently exploring Scala, Spark/MLlib, and graph analytics to deepen his distributed-processing toolkit.
11 years of coding experience
9 years of employment as a software developer
The University of Arizona