Explore mixed models in genomic analyses and imaging studies through this informative conference talk by Francesco Paolo Casale at the Computational Genomics Summer Institute (CGSI2023). Delve into efficient set tests for genetic analysis of correlated traits, Gaussian process prior variational autoencoders, and linear mixed-model approaches to study multivariate gene-environment interactions. Discover how convolutional neural networks can accurately quantify histologic features in medical imaging, specifically for non-alcoholic steatohepatitis. Gain insights from Casale's research papers, covering topics from Nature Methods, Advances in Neural Information Processing Systems, Nature Genetics, and the Journal of Hepatology. Learn about cutting-edge applications of mixed models in computational genomics and medical image analysis during this 36-minute presentation.