Dr. Szpiro has been a faculty member in the Department of Biostatistics at the University of Washington since 2009, where he was also a post-doc from 2006 to 2009. He was the founding Program Director of the new MS Capstone program in the Department of Biostatistics.

A major focus of Dr. Szpiro's research is developing statistical methods for environmental epidemiology and applying these methods to studies of air pollution in major cohort studies such as MESA Air, the NIEHS Sisters Study, and the Women's Health Initiative. Current methodological projects include spatio-temporal exposure modeling, measurement error correction, optimal exposure prediction modeling for health effect inference, dimension reduction for spatially misaligned data, and control for unmeasured spatial and/or temporal confounding. A common conceptual theme is the role of random effects models in analyzing spatially or temporally structured data, in particular the importance of precisely characterizing sources of randomness/correlation in scientifically plausible data-generating mechanisms (as termed by Jim Hodges, "old-" vs "new-" style random effects).

Dr. Szpiro is the Data Center lead for the Environmental Influences on Child Health Outcomes (ECHO) Prenatal and Early Childhood Pathways to Health (PATHWAYS), and he collaborates on a variety of research projects in Global Health and Public Health, including optimizing HIV treatment uptake and outcomes in the developing world, cardiovascular outcomes in patients with chronic kidney disease, genetic association studies in Latino populations, and the relationship between vitamin D insufficiency and incidence of HPV.

Prior to coming to Seattle, Dr. Szpiro engaged in mathematical modeling and scientific consulting on national security issues (primarily air vehicle survivability and bio-terrorism defense) at MIT Lincoln Laboratory between 1999 and 2006. Dr. Szpiro received a Ph.D. and Sc.M. in Applied Mathematics from Brown University in 1999 and 1994, respectively, where he wrote his dissertation under the direction of Paul Dupuis on stochastic process based second-order numerical methods for Hamilton-Jacobi PDEs. He received a B.A. (Summa Cum Laude) in Mathematics from Revelle College at UC San Diego in 1993. During his undergraduate years, he had the opportunity to spend a few months tucked away at Oak Ridge National Laboratory proving interesting but almost certainly useless theorems about optimal saddle points for controlling biological systems.