
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 Additionally, cleaned up legacy code by removing outdated Lattice run.py examples, streamlining the codebase.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.
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.
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.

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