I research brain-like artificial neural networks for vision, create online tools for scientists, and share educational resources for programming and statistics.
I'm a computational neuroscientist working at the intersection of deep learning, primate visual neuroscience, human fMRI, and cognitive neuroscience. My thesis advisors were Profs. Dan Yamins and Kalanit Grill-Spector at Stanford University. My thesis focused on building deep neural network models that better match the function, structure, and development of visual cortex.
Learning topographic maps in deep convolutional neural networks
Ultra-high-resolution fMRI of human visual cortex
Understanding face recognition in deep neural networks
How are objects and scenes encoded in the lateral occipital complex?
Psychophysical analysis of object recognition under distinct kinds of noise
I create online tools that make information easier to find, store, access, and share.
These are some publicly available resources I’ve developed in various teaching roles. Please feel free to use and share them!