Learning high-dimensional Gaussian distributions is one of the most basic problems in statistical estimation, for which the simple approach of finding the empirical mean and covariance can be adequate. However, this simple approach does not satisfy the modern requirements of data analysis such as privacy and robustness. We explore some recent advances in understanding the sample complexity and computational complexity of learning Gaussians and their mixtures under these constraints.