His research is at the intersection of econometrics, economics, bioinformatics, statistics, and machine learning. In his research, he seeks to develop scalable and consistent methods to uncover the biological causes of health and wealth inequalities. Socioeconomic status, intelligence, and health tend to go hand in hand, and are, to a considerable degree, transmitted from one generation to the next, leading to wealth and health inequalities. The underlying mechanisms are poorly understood. However, decades of twin studies have established that virtually every human trait is driven by an interplay of genes (and, by extension, biology) and environment. The increasing availability of large-scale genetic data allows researchers to better understand the biological causes of such disparities and how these drive their intergenerational transmission.
Ronald's research is at the forefront of this development. He designs new statistical methods (or combines existing ones in novel ways), such that the resulting methods (1) are sufficiently versatile to capture important real-life dynamics, (2) can handle data for hundreds of thousands of individuals on millions of so-called biomarkers, (3) take into account important sources of bias and/or inconsistency, and (4) are published with accompanying open-source tools, enabling other researchers to apply his methods to their data.
His work has been published in journals such as Nature Genetics, PLOS Genetics, the International Journal of Epidemiology, Nature Communications, and Nature Human Behaviour.
