Integrating population evolution and dynamics offers a promising avenue for understanding rapidly evolving pathogens. For example, during HIV infection, the virus can escape several immune response populations via resistance mutations at distinct epitopes (proteins coded in viral genome), precipitating a dynamic network of interacting virus and immune variants. Understanding the main factors shaping viral resistance pathways and immune dynamics is crucial for designing effective vaccines and immunotherapies. While the virus-immune interactions may be quite complex, I will talk about my recent work to link pathogen population genetics with dynamics theoretically and through data to characterize their evolution. We start with a general differential equation ecosystem model of multiple virus and immune populations, and then prove that different stable and persistent patterns emerge in the virus-immune network. Next, I will present two collaborative projects where the "eco-evolutionary" modeling framework is connected to genomic and population data. First, we describe the interaction between several immune cell populations and viral "quasi-species" sampled from experiments of the simian immunodeficiency virus (SIV)-infected macaque model of HIV infection. The second work concerns a mixed transmission dynamic model of the Cholera outbreak in Haiti, where aquatic reservoirs actively contribute to the epidemic. The mathematical models can recapitulate the data and shed light on pathogen evolution in both cases, along with motivating ongoing work on jointly deciphering the population genetics and dynamics of pathogens and their complex ecosystems.