Advancing women’s health through data science

We work in the broad research areas of high dimensional data, measurement error and network models for metabolomics datasets. Our work is motivated by ongoing collaborations in metabolomic studies nested within the Women’s Health Initiative and the Nurses’ Health Study cohorts, with a focus on cardiovascular disease, stroke and mental health.


With application to data generated in metabolomic studies, our recent work has centered on variable selection/prediction in matched case-control studies, handling non-random censoring in metabolomics and in network models.

Misclassification/Measurement Error

Our work has involved developing statistical models and software for the design and analysis of studies that collect self-reported outcomes. Self-reports are attractive instruments for collecting prevalent and incident disease outcomes in large-scale, epidemiologic cohorts.

Early Detection of HIV Infection in Infants

We are currently involved in an international, multi-cohort collaboration to evaluate the properties of diagnostic tests used in early infant diagnosis of HIV infection, particularly in the context of maternal regimens involving highly active antiretroviral therapy (HAART).
Partially funded by NIH grants R21HD072792 from NICHD, R01HL122241 from NHLBI, and R01LM013444 from NLM.