Published on Nov. 27, 2023

Updated on Dec. 19, 2023

Sue Brownawell is pursuing an informatics PhD at the University of Missouri, in the Institute for Data Science and Informatics. Her research interests aim to advance methods in causal inference though geospatial context, resulting in novel approaches for inferring causality. Geospatial information is a key factor affecting the proliferation and evolution of diseases; spatial context includes the critical, interdependent factors of air, water, and soil parameters, socio-economic characteristics, human geography, and infrastructure. Analyzing these factors enlightens our understanding of disease incidence and informs sound policy in its management and control. Though most models in data science and informatics are causal in nature, the answers most models give are not. Associations, correlations, and patterns can be useful, but sometimes they can also be misleading. Hence, a focus on evaluating the suitability of data and the appropriate model design for inferring causality in a spatial context is needed.

Sue enjoys facilitating others’ learning. She instructs boot camps in R and Python for the Master of Data Science program in the Institute. Her role also provides technical support and guidance for students and instructors using Jupyter, git, and PostgreSQL. Sue continuously seeks to enhance her role as an instructor through rich collaborations and a deeper skillset applying the tools of data science and informatics.