Hrayr is a Ph.D. candidate at University of Southern California advised by Prof. Aram Galstyan and Prof. Greg Ver Steeg. He does both applied and theoretical research on some aspects of deep learning, often taking an information-theoretic perspective. His main research directions are (a) studying information stored in neural network weights or activations and its connections to generalization, memorization, stability and learning dynamics; and (b) representation learning with the goal of enriching the learned representation with useful properties, such as minimality, disentanglement, modularity, reduced synergy, etc. More broadly, Hrayr is interested in designing novel representation learning approaches, unsupervised/self-supervised learning, studying the generalization phenomenon of deep neural networks, and in estimation/approximation of information-theoretic quantities or their alternatives.