Kris Dewandeler focused on stabilizing the feature selection workflow in the datakind/student-success-tool repository, addressing a critical issue related to perfect multicollinearity during iterative feature removal. By implementing a robustness fix using Python, NumPy, and Pandas, Kris ensured that when all initial Variance Inflation Factor (VIF) values are infinite, the workflow now logs a diagnostic message and returns the original DataFrame, preventing unnecessary feature drops and preserving data integrity. This approach improved model reliability in high-correlation scenarios and reduced downstream debugging time. The work demonstrated careful defensive programming and enhanced observability for troubleshooting complex data science pipelines.