Hugo Latourelle-Vigeant

Welcome to my academic website! I am an incoming Ph.D. student at Yale University’s Statistics and Data Science program. I recently completed my Master’s degree in Mathematics and Statistics at McGill University, where I had the privilege of working under the co-supervision of Professor Courtney Paquette and Professor Elliot Paquette. Prior to this, I earned my B.Sc. in Mathematics and Computer Science with First-Class Honours, also from McGill University.

My academic interests revolve around the fascinating realm of large random systems. I see data science and machine learning as exciting avenues for theoretical exploration, using tools from random matrix theory, high-dimensional probability, optimization and statistics, into the complexities of high-dimensional systems. My research has led me to study theoretical aspects of random matrix theory, notably the matrix Dyson equation. I have also explored the applications of random matrix theory to machine learning, notably by establishing a Gaussian equivalence result for the empirical test error of random features ridge regression. For a slightly more detailed discussion, please refer to the “research” tab.

Beyond my academic pursuits, I like to engage in physical activities. Snorkeling, kayaking, and skiing are among my favorite pastimes. In an alternate reality, I might have been known as a “gym bro.”