Prof. Wasim Huleihel

My research deals primarily with learning theory and its connections to information theory and modern statistics. In particular, my goal is to develop statistical bounds (for algorithms without computational constraints) and computational bounds (with constraints) for high-dimensional problems with hidden structure. In a wide variety of such problems, there is a significant gap between the performance of algorithms from these two families, and my research aims to explain why and when these gaps exist. Furthermore, alongside theoretical research, I also study problems with practical implications (such as ranking and recommendation systems), as well as practical work with data.