I'm a postdoctoral researcher working with Eero Simoncelli at the Flatiron Institute. My work brings together ideas from computer vision, machine learning, information theory, and Bayesian statistics. My primary goal is to establish mathematical principles from which we could derive reliable models of human perception (e.g., developing a theory for unsupervised learning of perceptual image quality metrics). I also apply these principles to design more effective algorithms and evaluation criteria for low-level vision tasks, such as image restoration and compression.

Previously, I received a PhD in Computer Science from the Technion—Israel Institute of Technology, where I was advised by Michael Elad and Tomer Michaeli. My doctoral research focused on designing image restoration and compression algorithms that are based on generative models, and on studying their theoretical limits. You can download my PhD dissertation from this link.