Health disparities, be it racial, economic, rural-urban, gender- or age-based, have come to the forefront across the world. To elucidate the biological, social, economic and psychological mechanisms of health disparities, and to develop interventions that engage community in targeting these mechanisms to reduce health disparities, it is necessary to work with complex multidimensional datasets containing molecular, genetic and biometric information from individuals, plus their socioeconomic status, local environment/safety, degree of segregation, access to medical care/education, and levels of pollution. We are developing novel statistical and ML approaches to harmonize these heterogeneous data and detect important contributors to health disparities. We are aiming to develop predictive tools to identify populations at-risk for poor health outcomes, in order to help community services, reach out and bring in those individuals for treatment earlier.
The REU student will work with NCSA computational scientists and faculty collaborators in areas of women’s health and infectious diseases, as well as the representatives of the public health district, to gather, prepare and analyze health-related data, and develop novel statistical and ML approaches.