I completed my PhD with Dr. Zaidi at SUNY’s Graduate Center for Vision Research, where I studied psychophysics and computational modeling of 3D shape perception, object rigidity and non-rigidity, and perceptual distortions in amblyopia. This work uncovered computational principles of when and why the human visual system operates sub-optimally, which sparked my interest in how humans learn to see.
My current research focuses on how humans acquire visual representations in an unsupervised manner that enables them to navigate the world. The fovea, which provides the highest resolution in our visual field, spans only about 1° out of the ~220° field of view. Yet our perception feels seamless, as if high resolution were available everywhere. One possibility is that we continuously predict foveal detail from peripheral input, a process that requires knowledge of the statistical structure of natural scenes.
I aim to implement this unsupervised learning paradigm in Vision Transformers, testing the models both on task performance and their ability to predict human perceptual phenomena such as change blindness. I also plan to compare the models’ internal representations with neural data to better understand the link between visual learning in humans and artificial systems.