I study representation learning across humans and machines—combining computational modeling with cognitive science to understand how both systems see and understand the world.
As a PhD candidate at the Max Planck Research Group Vision and Computational Cognition, supervised by Martin Hebart, I work on interpretable alignment of representations—bridging cognitive science and machine learning in both directions. I examine similarities and differences between human and artificial systems, primarily in vision but drawing from language and other domains.