Biography

Vision is an active process. Far from being passive recipients of external information, our visual systems are constantly generating meaning by combining sensory information with internal beliefs about the structure of the world around us. From the perspective of Bayesian statistics, these beliefs correspond to perceptual priors. My research interests center around uncovering the hidden structure of these priors and understanding the role that they play in perception and cognition. More broadly, I explore the behavioral, computational, and neural (biological) components of bayesian visual decision making. I also investigate how biological vision systems differ from artificial ones. For a representative example of my work probing visual working memory priors, see our recent publication in PNAS. In this line of work, I use experimental methods based on serial reproduction combined with Bayesian computational modeling to reveal shared perceptual priors and representations in human visual memory. In a second line of work, I compare human vision to contemporary Artificial Neural Networks (ANNs). For a representative example of my current activities in this area, see our oral presentation and paper in NeurIPS 2021. I completed my Ph.D. in Thomas Griffiths’ Computational Cognitive Science Lab at UC Berkeley in August of 2018. Prior to completing my Ph.D., I completed an M.S. in Computer Science (EECS), also at UC Berkeley. I then joined Princeton University as a postdoctoral researcher in the Computational Cognitive Science Lab. During that time, I was also affiliated with the Computational Auditory Perception Research Group in the Max Planck Institute for Empirical Aesthetics, where I was co-advised by Nori Jacoby. I am currently a postdoctoral fellow at UT Austin in the Center for Perceptual Systems (CPS), where I am working on uncovering the neural correlates of Bayesian computation in visual decision making.