Learning from Few Crashes

Critical deformation behaviours are, by definition, rare. Supervised learning starves without labels.

My thesis built a robust data representation and evaluated few-shot and self-supervised algorithms to learn from very little — improving consistency in identifying critical components across different crash studies.

The goal was practical: save engineers time, reduce cost, and catch the deformation that matters earlier.