Description

During the CCAM lunch seminar this week, we will participate in a group activity to meet each other, share resources, and identify folks in the CCAM community who have experience with conferences, workshops, internships, grants, or other resources that you may be interested in for the future. The seminar will take the form of an interactive bingo activity, where folks will be asked to find others in the group who have experience with a range of mathematical resources and activities (i.e., AMS Mathematics Resource Communities, different NSF research institutes and travel grants, teaching various classes, industry applications, etc). This activity has been used during icebreaker events at past SIAM conferences, including the 2024 SIAM Annual Meeting and the 2025 SIAM Conference on Applications of Dynamical Systems.  

Title: Multi-objective Bayesian inference in a stochastic agent-based model of zebrafish pattern formation via topological data analysis

Abstract

In many biological settings, agent-based models provide a natural framework for capturing stochasticity and individual-level interactions amongst large number of cells. However, inferring the parameters in such models poses significant challenges that limit the predictive power of these models. We demonstrate that combining topological data analysis with approximate-approximate Bayesian computation is a computationally feasible approach to addressing these challenges. In our study, we focus on an existing agent-based model of pattern formation in zebrafish skin, and we show how to estimate parameters and perform identifiability analysis in this complex, stochastic model. In particular, we show how to use multi-objective inference to combine multiple biologically meaningful summaries of model output to be able to accurately determine parameter values.

Title: Data-Driven Closure Models (DDCMs)

Abstract

Data-driven closure models (DDCMs) are machine learning models that are embedded with computational simulations with the overall goal of capturing "the missing physics" (closure term) of a system. Data-assimilation uses statistical techniques to create more accurate and reliable predictions in complex systems, by combining real-world observations with computational simulations. In this research, we used an SIQR exemplary model as our physical simulation with closure term to be estimated. This, because we want to capture the closure term as accurately as possible, so that the entire model is accurate as a whole. We provide a brief, informal introduction to data assimilation, and present the preliminary results of the sensitivity analysis of the estimate of an algorithmic parameter for data assimilation.