Yimei Li is a fifth-year Ph.D. student in Quantitative and Computational Biology at Princeton University, in the process of earning a certificate from the Center for Statistics and Machine Learning. She joined the Levin Lab in May 2022, where she began studying mathematical models for the ecology and evolution of infectious diseases, especially their cross-scale dynamics. Before that, she worked on dynamical models of dengue incidence. She is interested in human–virus interactions and combines machine learning and AI with mechanistic modeling to follow viruses across scales: what genome sequence data say about replication in cells, how that sets the course of infection in a whole host, and what that means for transmission and population-level policy. Her current work develops machine learning methods for outbreak forecasting, applies AI to virus dynamics across scales, and investigates defective interfering particles (DIPs) in influenza — their role in evolution, immune responses, and therapeutic potential.