google/deepvariant
Dec 2024 – Dec 2024
1 Month active
Languages Used
C++Python
Technical Skills
Data AnalysisDeep LearningKerasMachine LearningSoftware EngineeringTensorFlow
tsushi
PROFILE
Tsushi
Worked on the google/deepvariant repository to deliver Variant Type Aware Metrics and Evaluation, enabling granular performance assessment for SNP and INDEL variant types. Developed a dedicated metrics module and updated data providers to include variant type information, allowing for specialized calculations and more detailed model evaluation. Enhanced the training and tuning pipelines to leverage these new metrics, supporting more precise model selection and actionable business insights. Utilized Python, TensorFlow, and Keras to implement these features, focusing on data analysis and deep learning techniques. The work provided a foundation for variant-type specific evaluation, improving the interpretability of model performance.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
234
Activity Months1
Your Network
4716 peopleSame Organization
@google.com4703
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Shared Repositories
13
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Concise monthly summary for 2024-12 focusing on google/deepvariant. Delivered Variant Type Aware Metrics and Evaluation, enabling SNP/INDEL-specific metrics, updating data providers, and introducing a dedicated metrics module. Training and tuning pipelines updated to use these metrics, enabling granular performance evaluation by variant type and actionable business insights. Commit: 2043b96d23ce3e773ddd0b38dfd7df6b0fd89043.
1 Commits • 1 Features
Dec 1, 2024Concise monthly summary for 2024-12 focusing on google/deepvariant. Delivered Variant Type Aware Metrics and Evaluation, enabling SNP/INDEL-specific metrics, updating data providers, and introducing a dedicated metrics module. Training and tuning pipelines updated to use these metrics, enabling granular performance evaluation by variant type and actionable business insights. Commit: 2043b96d23ce3e773ddd0b38dfd7df6b0fd89043.
December 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Data AnalysisDeep LearningKerasMachine LearningSoftware EngineeringTensorFlow
Repositories Contributed To
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