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Sealybla

PROFILE

Sealybla

Developed and delivered the Matrix Product Matrix Chain (MPMC) module for the QMCSoftware/QMCSoftware repository, implementing a modular MPNN-based architecture with core model, training scripts, and utilities to support advanced experimentation in scientific computing. Focused on maintainability and extensibility, the work included code refactoring, improved sample generation, and robust hyperparameter handling using Python and PyTorch. Enhanced reliability by strengthening error handling for C library loading, reducing runtime failures. Additionally, cleaned up legacy code by removing outdated Lattice run.py examples, streamlining the codebase. Demonstrated depth in deep learning, graph neural networks, and numerical methods within a well-structured software design.

Overall Statistics

Feature vs Bugs

67%Features

Repository Contributions

15Total
Bugs
1
Commits
15
Features
2
Lines of code
1,506
Activity Months1

Work History

June 2025

15 Commits • 2 Features

Jun 1, 2025

June 2025 monthly summary: Delivered the Matrix Product Matrix Chain (MPMC) module with core model, training, and utilities; cleaned up legacy examples; and hardened C library loading. Strengthened code organization, maintainability, and reliability to accelerate experimentation and reduce runtime errors.

Activity

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Quality Metrics

Correctness81.4%
Maintainability82.8%
Architecture80.0%
Performance68.0%
AI Usage22.6%

Skills & Technologies

Programming Languages

C++JinjaPythonTorch

Technical Skills

Code CleanupCode RefactoringDeep LearningError HandlingGNNGNNsGraph Neural NetworksMachine LearningNumerical MethodsObject-Oriented ProgrammingPackage ManagementPyTorchPythonRefactoringScientific Computing

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

QMCSoftware/QMCSoftware

Jun 2025 Jun 2025
1 Month active

Languages Used

C++JinjaPythonTorch

Technical Skills

Code CleanupCode RefactoringDeep LearningError HandlingGNNGNNs