RosettaCommons/foundry
Dec 2025 – Dec 2025
1 Month active
Languages Used
MarkdownPDBPythonYAML
Technical Skills
Bug FixingCode FormattingConfiguration ManagementData ManagementDocumentationPython
Aiko Muraishi
PROFILE
Aiko Muraishi
During December 2025, Akira Imura enhanced the RosettaCommons/foundry repository by developing advanced symmetry modeling features and optimizing inference performance for low-memory environments. Akira introduced new symmetry example PDB files and expanded the is_non_loopy attribute to clarify conditional and unconditional symmetry handling, improving modeling accuracy and documentation. Addressing robustness, Akira fixed legacy input parsing and refined error handling in the inference engine, increasing reliability. Performance was further improved by eliminating a for loop in the chunked pairwise layer and enabling multi-batch support. These contributions, implemented in Python and YAML, demonstrated depth in algorithm design, data processing, and software maintenance.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
2
Lines of code
17,905
Activity Months1
Your Network
146 peopleSame Organization
@uw.edu121
Shared Repositories
25
Rafael BrentMember
AndrewKubaneyMember
Jasper ButcherMember
AikoMember
Daan KrolMember
Hope WoodsMember
David Hyunyoo JangMember
JaneMember
Ubiquinone-dotMember
Work History
December 2025
8 Commits • 2 Features
Dec 1, 2025December 2025 monthly summary for RosettaCommons/foundry: Delivered significant symmetry modeling enhancements and memory-efficient inference, fixed core parsing and error handling issues, and improved performance. Strengthened modeling accuracy, reliability, and throughput on low-memory hardware.
8 Commits • 2 Features
Dec 1, 2025December 2025 monthly summary for RosettaCommons/foundry: Delivered significant symmetry modeling enhancements and memory-efficient inference, fixed core parsing and error handling issues, and improved performance. Strengthened modeling accuracy, reliability, and throughput on low-memory hardware.
December 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability92.6%
Architecture92.6%
Performance90.0%
AI Usage22.6%
Skills & Technologies
Programming Languages
MarkdownPDBPythonYAML
Technical Skills
Bug FixingCode FormattingConfiguration ManagementData ManagementDocumentationPythonPython programmingSoftware EngineeringSoftware Maintenancealgorithm designdata analysisdata modelingdata processingdocumentationmachine learning
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