Speakers
Description
Agent-based models (ABMs) are a natural platform to capture the complexity inherent in multiscale biological systems. However, the impact of mathematical modeling with ABMs remains limited by persistent challenges in model calibration, sensitivity analysis, and uncertainty quantification. Difficulties in integrating experimental data with models and the computational cost of simulating ABMs leads to challenges in rigorous comparison and prediction. This minisymposium will highlight an assortment of methods for analyzing data-driven ABMs applied to various biological settings ranging from cancer growth, ecological population dynamics, and epidemiology. Our speakers span a range of career stages, from graduate students to full professors, and reflect a diversity of identities, perspectives, and geographical locations.
