Alexandria Pokorny
Lead Software Engineer at State Farm ®
Greater Bloomington, Illinois Area United States
Summary
👤
Senior🎓
Top SchoolAlexandria Pokorny is a Lead Software Engineer with eight years of experience delivering machine learning solutions and cloud-native APIs, currently shaping autonomous product teams at State Farm. She combines hands-on expertise in Java, Python, Spring Boot, PCF and AWS with a penchant for Terraform and CI/CD pipeline challenges to consistently move models from prototype to production. Her background in biomathematics and work on PlantCV — where she implemented and rigorously tested a color-correction submodule for high-throughput plant phenotyping — shows a strong bridge between research-grade image processing and enterprise software. An enthusiastic mentor and collaborative team-builder, she thrives on tricky code and continuous learning. Off-platform, she translates the discipline of a former circus aerialist into resilient leadership and an overcommitted garden.
8 years of coding experience
3 years of employment as a software developer
Postbaccalaureate, Computer Science, Postbaccalaureate, Computer Science at Fontbonne UniversityBachelor's degree, Mathematics, Bachelor's degree, Mathematics at Illinois State University
Github Skills (13)
algorithm10
image-analysis10
algorithms10
computer-vision10
colours10
opencv10
python10
numpy10
scientific-computing9
bioinformatics8
testing8
pytest8
documentation7
Programming languages (2)
Github contributions (5)
danforthcenter/plantcv
May 2018 - Aug 2018
Plant phenotyping with image analysis
Role in this project:
Data Scientist
Data ScientistContributions:10 commits, 3 PRs, 3 pushes in 2 months
Contributions summary:Alexandria primarily contributed to the `plantcv` repository by developing and testing a color correction subpackage. Their work involved implementing functions for calculating color matrices, Moore-Penrose inverses, transformation matrices, and applying these transformations to images. The user also wrote extensive tests, ensuring the accuracy and functionality of the color correction algorithms. Furthermore, they created documentation to support the subpackage, aiding in understanding and usability.
plantimage-analysissciencephenotypingbioinformatics
aapokor/Code-Samples
Nov 2018 - Sep 2023
Contributions:2 PRs, 16 pushes, 3 branches in 4 years 10 months
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