Basic separation of concerns exists across files (generation, algorithms, analysis), but heavily relies on procedural scripts rather than reusable components.

Employs memory-inefficient anti-patterns, such as appending to pandas DataFrames inside nested loops, leading to high time complexity.

Prevalent hardcoded variables, static paths, and repetitive code blocks severely hinder reproducibility, onboarding, and scaling.

Competent in implementing complex scientific logic, but struggles with advanced idiomatic patterns like efficient DataFrame concatenation or DRY principles.

Effectively utilizes pandas, scipy, and networkx for complex graph theory manipulation and curve fitting in an academic context.

Capable of implementing research-grade quantum algorithms using pyquil to evaluate the Quantum Approximate Optimization Algorithm (QAOA).

Codebase shows high duplication, hardcoded configurations, procedural execution, and a lack of automated testing.

Demonstrates safe deserialization practices by using ast.literal_eval over unsafe eval() for parsing complex CSV structures.