starsimhub/starsim
Jul 2025 – Jul 2025
1 Month active
Languages Used
Python
Technical Skills
Class MethodsCode RefactoringDemographic ModelingDemographics SimulationNumPyNumba
Kelvin Burke
PROFILE
Kelvin Burke
Developed two core features for the starsimhub/starsim repository, focusing on enhancing simulation performance and demographic modeling accuracy. Improved the starsim.time module by optimizing date and duration operations, strengthening error handling, and introducing numeric validation for safer time vector initialization. Designed and integrated timeprob-based utilities to convert demographic rates into per-timestep probabilities, directly supporting births, deaths, and pregnancy calculations. Applied Python, NumPy, and Numba to achieve faster simulations and more reliable results. Refactored array utilities for maintainability and expanded unit testing coverage, ensuring robust, validated time series analysis and simulation workflows throughout the codebase without introducing new bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
2
Lines of code
158
Activity Months1
Your Network
15 peopleShared Repositories
15
Work History
July 2025
16 Commits • 2 Features
Jul 1, 2025July 2025 highlights for starsim: two major feature milestones with a focus on performance, robustness, and probabilistic modeling for per-timestep simulations. Implemented Time Core Performance and Robustness Improvements in the starsim.time module, resulting in faster date/time operations, safer time vector initialization, stronger error handling, and more efficient duration arithmetic. Introduced Timeprob-based Demographic Calculations and Probability Modeling to accurately convert rates to per-timestep probabilities and integrated it into births, deaths, and pregnancy calculations, with accompanying performance improvements. Strengthened stability with additional numeric validation, tests, and targeted refactors across time and arrays utilities. Demonstrated strong Python performance optimization, NumPy vectorization, and probabilistic modeling skills, delivering tangible business value through faster simulations, more reliable results, and improved demographic accuracy.
16 Commits • 2 Features
Jul 1, 2025July 2025 highlights for starsim: two major feature milestones with a focus on performance, robustness, and probabilistic modeling for per-timestep simulations. Implemented Time Core Performance and Robustness Improvements in the starsim.time module, resulting in faster date/time operations, safer time vector initialization, stronger error handling, and more efficient duration arithmetic. Introduced Timeprob-based Demographic Calculations and Probability Modeling to accurately convert rates to per-timestep probabilities and integrated it into births, deaths, and pregnancy calculations, with accompanying performance improvements. Strengthened stability with additional numeric validation, tests, and targeted refactors across time and arrays utilities. Demonstrated strong Python performance optimization, NumPy vectorization, and probabilistic modeling skills, delivering tangible business value through faster simulations, more reliable results, and improved demographic accuracy.
July 2025
Activity
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Quality Metrics
Correctness82.4%
Maintainability83.8%
Architecture78.2%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Class MethodsCode RefactoringDemographic ModelingDemographics SimulationNumPyNumbaNumerical ComputingObject-Oriented ProgrammingPandasPerformance OptimizationProbability CalculationPythonRate ConversionSimulationSoftware Development
Repositories Contributed To
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