Paul A. Parker

Paul A. Parker is an Assistant Professor in the Department of Statistics at the University of California, Santa Cruz, specializing in Bayesian methodology, official statistics, and modeling of dependent data structures. His work bridges advanced statistical theory with critical applications in government surveys, environmental monitoring, and social sciences.

Education:

• Ph.D. in Statistics, University of Missouri, 2021
• M.A. in Statistics, University of Missouri, 2018
• B.S. in Applied Mathematics, University of Idaho, 2014

Dr. Parker's research integrates Bayesian frameworks with modern machine learning to address challenges in spatial, temporal, and functional data analysis. He develops innovative models for small area estimation under informative sampling, with significant contributions to official statistics through applications like the U.S. Census Bureau's Household Pulse Survey and soil health assessment protocols. His methodological work enables more accurate analysis of complex survey data while accounting for heteroskedasticity and non-Gaussian structures.

Recent publications reveal a strong pattern of applying spatial deep learning and heterogeneous modeling to environmental monitoring (soil carbon mapping) and government statistics (health insurance coverage estimation). His work consistently focuses on computational efficiency for real-world implementation in federal statistical systems.

Scientific Awards:

• U.S. Census Bureau Dissertation Fellowship
• University of Missouri Population, Education and Health Center Interdisciplinary Doctoral Fellowship

Dr. Parker actively mentors graduate researchers including current PhD candidates Ethan Pawl, Qi Wang, and Sho Kawano, and former MS students Adam Slivinsky and Jacobo Pereira-Pacheco. His research is supported by grants from federal statistical agencies, particularly the U.S. Census Bureau, focusing on improving small area estimation methodologies for national surveys. He collaborates extensively with agricultural scientists through the Soil Health Assessment Protocol and Evaluation (SHAPE) project, developing statistical frameworks for dynamic soil health indicators.

His work with the U.S. Census Bureau's Center for Statistical Research and Methodology continues to influence methodological standards for official statistics, particularly in longitudinal survey design and spatial modeling of environmental data.