Postdoctoral Fellow · Biostatistics · Johns Hopkins
Jesus E. Vazquez
I develop statistical methods for incomplete and distributed data, with applications across neurological, pulmonary, and cardiovascular health.

About
I am a Postdoctoral Fellow in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, where I hold the Johns Hopkins Provost Postdoctoral Fellowship under Dr. Elizabeth A. Stuart. I completed my Ph.D. in Biostatistics at the University of North Carolina at Chapel Hill under Dr. Tanya P. Garcia. My dissertation developed robust and efficient estimators for regression models with right-censored covariates, with applications to Huntington disease progression.
My research focuses on settings where data are incomplete or distributed: censoring, missingness, and data that cannot leave the institutions that collect them. Currently, I am working on federated learning frameworks that let multiple clinical sites draw joint inferences without sharing individual-level data. I collaborate across health domains, including neurological disease, pulmonary health, preterm kidney health, cardiovascular outcomes, and physical activity.
On the personal side, I consider myself a fronterizo, as I grew up on both sides of the US-Mexico border (Chihuahua and New Mexico). My favorite board game is Catan, pineapple goes on pizza, and one of my favorite quotes is “De aquí y de allá,” which translates to “from here and from there.” I really like this quote because it allows us to fully embrace our Latino heritage and also welcomes our experience growing up here in the US: we can be part of both cultures.
Get in touch
Open to new collaborations
Students: please send a brief email with your research interests, your resume, and your CV. Researchers from other fields are welcome to write about a project.
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All papers by areaRecent papers
Full publication list- How Estimators Change When Adapted from the Missing Covariate Problem to the Right-Censored Covariate ProblemPublished International Statistical Review, 2026 DOI
- Perinatal predictors of abnormal adolescent cardiovascular-kidney health after extremely preterm birth: A machine learning analysis of the Extremely Low Gestational Age Newborn–Environmental Child Health Outcomes (ELGAN-ECHO) cohortPublished Kidney International, 2026 DOI
- Higher educational attainment in Huntington disease families: evidence from the Enroll-HD studyPublished Orphanet Journal of Rare Diseases, 2026 DOI
- Federated Learning with Missing Data: A Weighted Approach with Variance CorrectionUnder review Statistics in Medicine, expected 2026 arXiv
- Robust Estimation under Outcome Dependent Right Censoring in Huntington Disease: Estimators for Low and High Censoring RatesUnder review Statistics in Medicine, expected 2026 arXiv