
During December 2025, Tom Latham developed and packaged the Laura++ Maximum Likelihood Dalitz-Plot Fitting Tool within the conda-forge/staged-recipes repository, focusing on robust build automation and deployment readiness. He implemented build, install, configuration, and test scripts using C++, Bash, and YAML, ensuring the tool’s compatibility across multiple platforms. In conda-forge-pinning-feedstock, Tom extended cross-architecture migration for laura, enabling support on osx-arm64, linux-aarch64, and linux-ppc64le. His work addressed the need for advanced statistical analysis tooling and broadened runtime accessibility, demonstrating depth in continuous integration, DevOps practices, and multi-architecture packaging without introducing new bugs during the period.
December 2025 performance summary: Delivered two key features across two repositories, expanding analytics capabilities and platform reach. Highlights include introduction of the Laura++ Maximum Likelihood Dalitz-Plot Fitting Tool in conda-forge/staged-recipes, with build/install/config/test configurations, and extension of conda-forge-pinning-feedstock to include cross-architecture migration for osx-arm64, linux-aarch64, and linux-ppc64le. While no critical bugs were documented for this period, the work delivered tangible business value by enabling advanced data analysis tooling and expanding runtime support across major architectures. Accomplishments include packaging and deployment readiness, improved platform compatibility, and foundational ML/statistical tooling that can be leveraged by downstream users. Technologies/skills demonstrated include: maximum likelihood fitting, Dalitz-plot analytics, packaging/build scripts, CI/test configuration, and multi-arch migration and platform tagging.
December 2025 performance summary: Delivered two key features across two repositories, expanding analytics capabilities and platform reach. Highlights include introduction of the Laura++ Maximum Likelihood Dalitz-Plot Fitting Tool in conda-forge/staged-recipes, with build/install/config/test configurations, and extension of conda-forge-pinning-feedstock to include cross-architecture migration for osx-arm64, linux-aarch64, and linux-ppc64le. While no critical bugs were documented for this period, the work delivered tangible business value by enabling advanced data analysis tooling and expanding runtime support across major architectures. Accomplishments include packaging and deployment readiness, improved platform compatibility, and foundational ML/statistical tooling that can be leveraged by downstream users. Technologies/skills demonstrated include: maximum likelihood fitting, Dalitz-plot analytics, packaging/build scripts, CI/test configuration, and multi-arch migration and platform tagging.

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