the kiang lab
Our mission is to identify, develop, and advocate for equitable ways to improve population health with a focus on structurally and medically vulnerable populations. Our research topics range from substance use to climate disasters. In pursuit of our mission, we use rigorous computational methods, machine learning, demographic techniques, and traditional epidemiologic analyses combined with social theory. All our work is applied with the goal of informing interventions and equitable policies.
We ask questions like: How have disparities in mortality changed during the COVID-19 pandemic? Which groups of schoolchildren are most exposed to wildfire smoke? How can we distribute COVID-19 vaccines more fairly? How has the COVID-19 pandemic impacted fatal drug poisonings? Broadly, our work falls into two, non-overlapping areas.
Social epidemiology. We investigate the way the structures of society permeate all aspects of health and result in an unjustly unequal distribution of health and disease. Our goal is to identify equitable policies and approaches to improving health among structurally and medically vulnerable populations.
Computational epidemiology. Rooted in social theory, we use computational methods to rigorously analyze and combine data from non-health data sources (such as mobile phone data or social media data) and traditional sources (such as health insurance claims or mortality data) to understand large-scale human behavior and its impact on health.
We love using new methods and working with exciting data, but ultimately, we are a solutions-driven lab focused on applied problems. In pursuit of this mission, we use a variety of computational (e.g., machine learning, microsimulations), demographic (e.g., decomposition and lifetable techniques), epidemiologic (e.g. dynamical models), and statistical tools (e.g., Bayesian spatial models) that are most appropriate for the research question.