The past decade has seen rapid advances in artificial intelligence methods applied to neuroimaging data, enabling increasingly precise characterization and prediction of brain aging across diverse populations. These technologies have allowed scientists to map the local progression of aging in remarkable detail, with important implications for medicine and society. In our laboratory, we integrate brain mapping techniques with machine intelligence and computational biology to investigate how brain aging progresses, how it is shaped by genetics and disease, and what it means for cognition. The lab has contributed key innovations to understanding brain aging in relation to factors such as genetics, sex, cognition, and neuroanatomy. Using multiple neuroimaging modalities, we have gained unique insights into the relationships among neural injury, brain plasticity, and neurodegenerative disease, many of which have been featured in science magazines and neuroscience textbooks. Our work with pre-industrial populations such as the Tsimané has also revealed distinct patterns of aging across diverse environmental contexts, further advancing a holistic understanding of brain aging.