Shahab's research is centered at the intersection of neuroscience and AI, also known as NeuroAI. Specifically, he examines visual perception and learning in both biological brains and artificial neural networks. He uses deep learning as a computational framework to model learning and perception in the brain, and leverages our understanding of the nervous system to create more biologically-inspired artificial intelligence.

Experimental cognitive science, computer models and simulations of cognitive processes and neuroimaging to determine why and how attention functions or fails.