Yixiao earned his PhD in Quantitative Life Sciences from McGill University, where he developed penalized (sparse) linear and nonlinear methods for automatic feature selection and interpretable prediction in high-dimensional biomedical datasets, specifically those with far more features than samples (N«p). His current work focuses on low-level optimization for terabyte- and petabyte-scale data-intensive computing: he is designing next-generation, ultrafast, and scalable linear mixed models capable of analyzing biobank studies of hundreds of thousands of individuals, effectively addressing challenges in both sample size (N) and dimensionality (p).