revbayes/revbayes
Oct 2024 – Mar 2025
4 Months active
Languages Used
C++LogRevMarkdownNEXUS
Technical Skills
C++Data ValidationDebuggingPhylogeneticsSoftware ConfigurationSoftware Development
Mike
PROFILE
Mike
Contributed to the revbayes/revbayes repository by developing and refining core features for phylogenetic modeling and simulation. Focused on improving algorithm robustness and runtime performance, this work included implementing an auto-tuning likelihood approximator and enhancing recursion stability in stochastic node graph traversal. Addressed critical bugs such as segmentation faults and stack overflows by refining C++ logic for dependency management. Expanded test coverage and simulation fidelity through advanced data exclusion and validation strategies, while also improving code readability and documentation scaffolding. Leveraged skills in C++, Bayesian inference, and statistical modeling to strengthen maintainability, testing readiness, and overall software reliability.
Overall Statistics
Feature vs Bugs
44%Features
Repository Contributions
12Total
Bugs
5
Commits
12
Features
4
Lines of code
4,095
Activity Months4
Your Network
14 peopleShared Repositories
14
Work History
March 2025
5 Commits • 2 Features
Mar 1, 2025Concise monthly summary for RevBayes development in March 2025 focusing on code quality, documentation scaffolding, and a critical bug fix, aligned with business value and maintainability.
5 Commits • 2 Features
Mar 1, 2025Concise monthly summary for RevBayes development in March 2025 focusing on code quality, documentation scaffolding, and a critical bug fix, aligned with business value and maintainability.
March 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for revbayes/revbayes focusing on testing robustness and simulation fidelity. Key work included expanding ignoreData() tests with complex structures and fixing the phylogenetic simulation likelihood path to restore intended behavior and accuracy.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for revbayes/revbayes focusing on testing robustness and simulation fidelity. Key work included expanding ignoreData() tests with complex structures and fixing the phylogenetic simulation likelihood path to restore intended behavior and accuracy.
November 2024
1 Commits
Nov 1, 2024In November 2024, the RevBayes project focused on hardening core graph traversal reliability in the revbayes/revbayes repository. The primary effort was a critical bug fix in the getOrderedStochasticNodes routine to prevent stack overflow and address a parent-child recursion issue, improving stability for models with large or complex stochastic node graphs.
1 Commits
Nov 1, 2024In November 2024, the RevBayes project focused on hardening core graph traversal reliability in the revbayes/revbayes repository. The primary effort was a critical bug fix in the getOrderedStochasticNodes routine to prevent stack overflow and address a parent-child recursion issue, improving stability for models with large or complex stochastic node graphs.
November 2024
October 2024
4 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary focusing on key accomplishments for the revbayes/revbayes project. Delivered a default auto-tuning mechanism for the likelihood approximator within the GeneralizedLineageHeterogeneousBirthDeathSamplingProcess to improve robustness and runtime performance. Fixed a critical segfault in dependency graph processing by ensuring correct handling of cycles via proper visited-node tracking in Model::getOrderedStochasticNodes. Updated test expectations to align with current behavior across BDSTP, FBD, and large normal model tests, reducing test fragility and maintenance.
October 2024
4 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary focusing on key accomplishments for the revbayes/revbayes project. Delivered a default auto-tuning mechanism for the likelihood approximator within the GeneralizedLineageHeterogeneousBirthDeathSamplingProcess to improve robustness and runtime performance. Fixed a critical segfault in dependency graph processing by ensuring correct handling of cycles via proper visited-node tracking in Model::getOrderedStochasticNodes. Updated test expectations to align with current behavior across BDSTP, FBD, and large normal model tests, reducing test fragility and maintenance.
Activity
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Quality Metrics
Correctness87.6%
Maintainability88.4%
Architecture81.6%
Performance78.4%
AI Usage21.6%
Skills & Technologies
Programming Languages
C++LogMarkdownNEXUSRev
Technical Skills
Algorithm ImprovementBayesian inferenceC++Code ReadabilityCode RefactoringData EngineeringData ValidationData exclusionDebuggingDocumentationPhylogeneticsSimulationSoftware ConfigurationSoftware DevelopmentSoftware Refactoring
Repositories Contributed To
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