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 structure of these priors and understanding the role that they play in perception and cognition. I use innovative experimental methods combined with Bayesian computational modeling in my work. 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 completed a postdoc at Princeton University in the Computational Cognitive Science Lab and the Computational Auditory Perception Research Group at the Max Planck Institute for Empirical Aesthetics (MPIEA). Next, I completed a postdoctoral fellow in the Center for Perceptual Systems (CPS) at UT Austin, where I explored the neural basis of Bayesian inference during vision. I am currently a postdoctoral associate at MIT where I am exploring applications of the Information Bottleneck (IB) to cognitive science. I am also affiliated with NYU as a visiting scholar.