Zach Butzin-Dozier, Ph.D.


Zach is a postdoctoral scholar in the Division of Biostatistics at the UC Berkeley School of Public Health working under Professors Alan Hubbard, Jack Colford, and Mark van der Laan. His work applies Targeted Machine Learning methodology to data from the National COVID Cohorts Collaborative (N3C) to evaluate protective interventions against Long COVID, including COVID-19 vaccination, selective serotonin reuptake inhibitors (SSRIs), metformin, and immune-modulating drugs. He led a research team that placed third in the NIH Long COVID Computational Challenge for building an ensemble machine learning model that predicted individual risk of Long COVID diagnosis. His previous work evaluated drivers of child growth and development in rural Bangladesh through the WASH Benefits study. He received his Ph.D. in Epidemiology and MPH in Epidemiology and Biostatistics from UC Berkeley.

Research interests: 
Causal inference, targeted machine learning, synthetic data, adaptive trial design, optimal treatment regimes, child development, stress neurobiology, and antimicrobial resistance