beast-dev/beast-mcmc
Sep 2025 – Dec 2025
3 Months active
Languages Used
Java
Technical Skills
Bayesian AnalysisError HandlingJavaMCMCSoftware DevelopmentXML Parsing
xji3
PROFILE
Xji3
Worked on the beast-dev/beast-mcmc repository, delivering features and fixes that improved evolutionary modeling and simulation accuracy. Focused on configurable model setups, robust XML parsing, and numerical stability in likelihood calculations, the work included enhancements to MMM alignment, coalescent simulators, and Skygrid likelihood computations. Leveraged Java, Bayesian analysis, and algorithm optimization to support arbitrary hidden classes, branch-specific substitution models, and safe matrix operations using EJML. Addressed edge cases in population modeling and eigen decomposition, resulting in more reliable statistical inference and maintainable code. Emphasized flexibility, correctness, and scalability, enabling smoother integration of future evolutionary biology modeling features.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
12Total
Bugs
3
Commits
12
Features
5
Lines of code
1,266,500
Activity Months3
Your Network
16 peopleSame Organization
@tulane.edu1
Work History
December 2025
2 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12 (beast-dev/beast-mcmc). Delivered foundational features and critical bug fixes that enhance numerical robustness and accuracy in evolutionary modeling and likelihood calculations. Result: safer, more scalable code ready for upcoming model enhancements and wider adoption.
2 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12 (beast-dev/beast-mcmc). Delivered foundational features and critical bug fixes that enhance numerical robustness and accuracy in evolutionary modeling and likelihood calculations. Result: safer, more scalable code ready for upcoming model enhancements and wider adoption.
December 2025
October 2025
6 Commits • 2 Features
Oct 1, 2025October 2025: Delivered major numerical accuracy improvements for eigenvalue handling and the differential mass matrix in evolutionary simulations within beast-mcmc, with robust support for complex and real eigenvalues and corrected matrix calculations. Introduced SequenceSimulator to enable branch-specific substitution models and a flexible site-rate provider, significantly increasing realism and adaptability of simulations. Achieved maintainability gains through deduplication cleanup and CI-aligned fixes for complex-eigenvalue handling. Overall, these efforts raise simulation fidelity, reduce edge-case risk, and accelerate model comparisons and inference tasks.
October 2025
6 Commits • 2 Features
Oct 1, 2025October 2025: Delivered major numerical accuracy improvements for eigenvalue handling and the differential mass matrix in evolutionary simulations within beast-mcmc, with robust support for complex and real eigenvalues and corrected matrix calculations. Introduced SequenceSimulator to enable branch-specific substitution models and a flexible site-rate provider, significantly increasing realism and adaptability of simulations. Achieved maintainability gains through deduplication cleanup and CI-aligned fixes for complex-eigenvalue handling. Overall, these efforts raise simulation fidelity, reduce edge-case risk, and accelerate model comparisons and inference tasks.
September 2025
4 Commits • 2 Features
Sep 1, 2025Monthly summary for 2025-09 focused on delivering configurable model setups, robustness in data processing, and accuracy improvements in population models within the beast-dev/beast-mcmc repository. Highlights include added configurability for MMM alignment, robust XML parsing error handling, accuracy-oriented refactoring in the coalescent simulator, and safe bounds handling in Skygrid likelihood computations. The work emphasizes business value through flexibility, reliability, and correctness of statistical inferences.
4 Commits • 2 Features
Sep 1, 2025Monthly summary for 2025-09 focused on delivering configurable model setups, robustness in data processing, and accuracy improvements in population models within the beast-dev/beast-mcmc repository. Highlights include added configurability for MMM alignment, robust XML parsing error handling, accuracy-oriented refactoring in the coalescent simulator, and safe bounds handling in Skygrid likelihood computations. The work emphasizes business value through flexibility, reliability, and correctness of statistical inferences.
September 2025
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Quality Metrics
Correctness90.0%
Maintainability83.4%
Architecture83.4%
Performance80.0%
AI Usage23.4%
Skills & Technologies
Programming Languages
Java
Technical Skills
Bayesian AnalysisError HandlingJavaJava programmingMCMCSoftware DevelopmentXML Parsingalgorithm designalgorithm developmentalgorithm optimizationback end developmentbackend developmentcode refactoringdata structuresevolutionary biology modeling
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