Daniel received his PhD from Colorado State University where he developed statistical methods for studying the relationship between maternal exposures to air pollution and birth and children’s health outcomes. His research interests include: statistical machine learning, functional regression, variable selection, effect heterogeneity, and causal inference, with an application to understanding the health effects of environmental exposures.
