Wildfire is globally important to climate change and is projected to increase in severity with it. Thus, improving our predictability and understanding of its spatial patterns and impacts on terrestrial vegetation dynamics are greatly needed, as well as our ability to quantify the tradeoffs between wildfire mitigation practices and greenhouse gas emissions. Our capabilities in wildfire modeling span across explicit wildfire modeling with a machine learning approach, modeling vegetation dynamics across multiple ecosystems and climates, and modeling impacts of climate-relevant policy scenarios on wildfire emissions and the terrestrial carbon balance (machine learning model & E3SM, ecosys, and CALAND, respectively). Here we present our recent research activities in these various sub-disciplines of wildfire science to demonstrate a selection of the breadth of research at Berkeley Lab.