In the ever-changing and complex world we live in, we are constantly bombarded with visual and language information, which we process and integrate to form our perception, memory, and understanding of the world.
My main research questions are
(1) How do the human mind and brain transform sensory-specific inputs into increasingly abstract spatial and conceptual representations?
(2) What can artificial networks teach us about human neural representations, and what can human neural representations teach us about how artificial networks should learn?
Below are the two main topics I am interested in with several subtopics and projects I am working on:
From retinotopic input to world-centered spatial representations
From two-dimensional inputs to three-dimensional spatial structure
From modality-specific experiences to shared semantic representations
Use ANN representations to characterize the spatiotemporal organization of human semantic representations
Discover latent dimensions of human brain representations with ANN-based data driven methods
Translate brain-ANN representational differences into more human-like ANNs