Laura Tomkins
Senior Catastrophe Research Analyst
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolLaura Tomkins is a Senior Catastrophe Research Analyst and atmospheric scientist with nine years of experience translating geospatial and radar data into risk insights. Currently based in Boston, she holds a PhD in Geospatial Analytics and combines deep domain expertise in meteorology with strong Python skills to build robust data products for catastrophe modeling. Her open-source contributions to the well-regarded Py-ART radar toolkit include image masking and convective/stratiform classification tools, reflecting a knack for turning research methods into production-ready code. Laura has progressed from academic research roles to industry positions at Karen Clark & Company and WTW, bringing both rigorous experimental design and practical implementation to complex hazard problems. Colleagues rely on her ability to bridge detailed atmospheric science with scalable geospatial analysis to improve model fidelity and decision-making.
9 years of coding experience
10 years of employment as a software developer
University of KansasDoctor of Philosophy - PhD Geospatial Analytics, Doctor of Philosophy - PhD Geospatial Analytics at North Carolina State University
English
Github Skills (8)
data-visualizations10
data-visualization10
data-visualisation10
python10
numpy10
digital-signal-processing10
data-analysis8
testing8
Programming languages (2)
Github contributions (5)
ARM-DOE/pyart
Dec 2021 - Dec 2022
The Python-ARM Radar Toolkit. A data model driven interactive toolkit for working with weather radar data.
Role in this project:
Data Scientist
Data ScientistContributions:1 review, 12 commits, 6 PRs in 11 months
Contributions summary:Laura primarily contributed to the development and testing of a function designed for image muting radar data. They implemented the `image_mute_radar` function, which allows for the masking of specific regions within radar data based on thresholds from another field, enhancing data visualization. They also created a unit test to validate the functionality of the `image_mute_radar` function, and an example to show how to use this functionality. Additionally, the user added a function to classify convective and stratiform events.
model-drivenpythonradar-processingradardata-driven-model
lauratomkins/pyart
Dec 2021 - Nov 2024
The Python-ARM Radar Toolkit. A data model driven interactive toolkit for working with weather radar data.
Contributions:93 pushes, 8 branches in 3 years
model-drivenpythonradardata-driven-modelradar-data
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