Department of Medicine

Epidemiology and Population Sciences Research


The Community is Our Patient


We study the distribution and determinants of health-related outcomes in specific populations. To that end, we conduct research in marginalized and vulnerable communities, including people of color, neighborhoods recovering from natural disasters and individuals genetically predisposed to illness. Not only are we describing the factors that determine communities' risks for developing diseases, but we're also pursuing the features that contribute to resilience. Our faculty and postdoctoral fellows are specialists in diverse fields:

  • Adult and pediatric oncology, including glioma, breast cancer, esophageal adenocarcinoma and lung cancer
  • Molecular and genetic epidemiology
  • Obesity and nutrition
  • Microbiota and gut flora
  • Statistical methods in epidemiology and risk prediction
  • Translational and multidisciplinary applications

Research Labs


Badr Lab

Item Definition

The Badr Lab develops, implements and disseminates psychosocial interventions to improve the health behaviors and quality of life of cancer patients and their caregivers at diagnosis and during treatment, during post-treatment survivorship and at end of life.

Thrift Lab

Item Definition

The Thrift Lab's research is focused on developing cancer prediction models and tools for cancer patient risk stratification. The lab also focuses on racial disparities in cancer incidence and outcomes.

Oluyomi Lab

Item Definition

The Oluyomi Lab focuses on the measurement of non-chemical and chemical environmental stressors, understanding the pathways through which these stressors are related to health, and examining how neighborhood social forces create disparities in exposures and health outcomes. The lab places particular emphasis on spatial epidemiology.

Li Lab

Item Definition

The Li Lab aims to elucidate the role of genetic and environmental etiological factors in cancer and to develop first-in-class therapeutic interventions targeting major oncoproteins and tumor antigens using artificial intelligence and immunologic approaches.

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