QMCSoftware/QMCSoftware
Jun 2025 – Jun 2025
1 Month active
Languages Used
C++CUDAPyTorchPython
Technical Skills
Code ManagementCode RefactoringDeep LearningDependency ManagementDocumentationEnvironment Management
Ally Pascual Kwan
PROFILE
Ally Pascual Kwan
Developed and integrated a neural network–based solution for generating low-discrepancy point sets within the QMCSoftware/QMCSoftware repository, introducing an MPMC class that leverages a Message Passing Neural Network architecture using PyTorch. The work encompassed implementing comprehensive discrepancy functions, interactive parameter setup, and sample generation workflows, all designed to enhance scientific computing applications. Refactored core discrepancy calculations and parameter handling for improved maintainability, while also removing obsolete draft files to streamline the codebase. Added detailed documentation and robust error handling around version and package management, demonstrating strong skills in Python, PyTorch, and code management throughout the development process.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
1
Lines of code
1,357
Activity Months1
Your Network
15 peopleShared Repositories
15
Work History
June 2025
11 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for QMCSoftware/QMCSoftware focusing on feature delivery, bug fixes, and business impact. Delivered the MPMC: Neural Network-based Low-Discrepancy Point Sets, introducing an MPMC class for generating low-discrepancy point sets via a Message Passing Neural Network (MPNN) with PyTorch integration. This work includes discrepancy functions, an interactive parameter setup, sample generation, and a robust environment/user-experience flow. Also refactored discrepancy calculations, parameter handling, and maintained MPMC draft files; added documentation to the MPMC class; and implemented robustness improvements around version/package error handling. Cleaned up draft/codebase by removing obsolete mpmc_draft.py to reduce maintenance overhead and potential confusion.
11 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for QMCSoftware/QMCSoftware focusing on feature delivery, bug fixes, and business impact. Delivered the MPMC: Neural Network-based Low-Discrepancy Point Sets, introducing an MPMC class for generating low-discrepancy point sets via a Message Passing Neural Network (MPNN) with PyTorch integration. This work includes discrepancy functions, an interactive parameter setup, sample generation, and a robust environment/user-experience flow. Also refactored discrepancy calculations, parameter handling, and maintained MPMC draft files; added documentation to the MPMC class; and implemented robustness improvements around version/package error handling. Cleaned up draft/codebase by removing obsolete mpmc_draft.py to reduce maintenance overhead and potential confusion.
June 2025
Activity
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Quality Metrics
Correctness78.2%
Maintainability77.2%
Architecture71.8%
Performance67.2%
AI Usage21.8%
Skills & Technologies
Programming Languages
C++CUDAPyTorchPython
Technical Skills
Code ManagementCode RefactoringDeep LearningDependency ManagementDocumentationEnvironment ManagementError HandlingMachine LearningNeural NetworksNumerical MethodsPyTorchPython ScriptingQuasi-Monte Carlo MethodsScientific ComputingScripting
Repositories Contributed To
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