Jennifer Starling

Jennifer Starling


Jennifer Starling is a statistician at Mathematica. She focuses on developing flexible Bayesian models for structured data, tree-based methods, and causal inference, with applications to public health and medicine.

Since joining Mathematica in 2020, Starling works on developing Bayesian methods and assessing impacts for Medicare payment options, including Primary Care First. She also works on the COVID-19 risk calculator 19andMe and leads a validation study to compare 19andMe with other online COVID-19 risk assessment tools. Before Mathematica, Starling’s research focused on developing Bayesian methods for stillbirth risk assessment, medication abortion efficacy, and pre-eclampsia. She also conducts research related to reproductive care access and telemedicine services.

Starling holds a Ph.D. in statistics from The University of Texas at Austin, an M.S. in statistics with concentration in biostatistics from Texas A&M University, and a B.S. in mathematics from Virginia Tech. Before joining Mathematica in 2020, she was a National Institutes of Health Biomedical Big Data Fellow. Her work has been published in Annals of Applied Statistics, American Journal of Public Health, and American Journal of Obstetrics & Gynecology, among others.

  • Bayesian modeling
  • Causal inference
  • Non-parametric tree-based methods
  • Program evaluation
  • Clinical decision support
Focus Area Topics
  • Health
  • Health Information Technology and Analytics
  • Population Health
  • Medicare
  • Medicaid and CHIP
  • State Health Policy
  • COVID-19

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