I am an academic researcher with a background in computer engineering and machine learning, focusing on understanding why models work, not only improving metrics. My work typically involves training multiple models under controlled experimental settings, analyzing feature importance, robustness, and generalization behavior, especially under imbalanced data and noisy conditions. I am particularly interested in bridging theoretical intuition with empirical validation, and in turning experimental observations into reusable research insights rather than one-off solutions.