Postdoctoral Fellow · Biostatistics · Johns Hopkins
Jesus E. Vazquez, Ph.D.
I develop statistical methods for incomplete and distributed data, with applications across neurological, pulmonary, and cardiovascular health.
About
I am a fronterizo from Chihuahua and New Mexico, and 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.
Recent News
Full publication list- April 2027Invited panelist for an ENAR Inclusion, Diversity, Equity, and Accessibility (IDEA) Committee webinar on helping students from non-traditional and underrepresented backgrounds navigate graduate school.
- October 2026Presented at the JHU Biostatistics Retreat: causal inference with a right-censored marker of disease progression.
- September 2026Paper published in Kidney International Reports: Perinatal predictors of abnormal adolescent cardiovascular-kidney health after extremely preterm birth: A machine learning analysis of the ELGAN-ECHO cohort.
- August 2026Presented at COMPSTAT in Athens, Greece: robust estimation under outcome dependent right censoring in Huntington disease.
- July 2026Presented at the International Biometric Conference in Seoul, South Korea: federated learning with incomplete data.
- June 2026Won 3rd place in the Johns Hopkins Postdoctoral Association 3-minute presentation competition.
- June 2026Paper published in International Statistical Review: How Estimators Change When Adapted from the Missing Covariate Problem to the Right-Censored Covariate Problem.
- June 2026Preprint posted on arXiv: Federated Learning with Missing Data: A Weighted Approach with Variance Correction.
- April 2026Paper published in Orphanet Journal of Rare Diseases: Higher educational attainment in Huntington disease families: evidence from the Enroll-HD study.
Research areas
All papers by areaGet in touch
Open to new collaborations
I enjoy working with students and collaborators on incomplete data, causal questions, and public health problems. Projects often grow into papers, and I like to write them together. Researchers from other fields are welcome too. Students can start on the Work with me page; everyone else can send an email.