Phylogenetic studies now routinely use genome-scale data of many loci to resolve evolutionary relationships. As the data have grown in size, there are not only increasing computational challenges to handle a massive amount of data but also increasing gaps in modeling sequence evolution to capture biological complexities. In this talk I will first exemplify our collaborative efforts with Nick Goldman’s group in solving substantial bottlenecks with inferring trees of a million SARS-CoV-2 genomes. Secondly, I will show another effort in relaxing the assumption of reversibility and its utility to root a tree in the absence of an outgroup. If time permits I will touch on a pilot project on the application of machine learning and its challenges.