Big Data alone can't solve our problems.  This deceptively simple principle guides Sam's research and is motivated by two key insights from his dissertation.  Sam found that first, data in the absence of testable hypotheses, quantitative theory, and statistical methods is rendered worthless, and second, that the appropriateness of the data for a given question is far more important than the quantity gathered. From these insights, Sam has developed a scientific research framework: to investigate pressing biological questions by integrating mathematical models and data with powerful statistical methods.

Sam's highly collaborative research has focused on a broad range of questions, from the effect of environmental toxins on behavior, genetics, and neural biology in rats to models of spatiotemporal variation in tree density and fruiting phenology in Neotropical forests. However, his primary research interests lie at the interface of population genetics, statistics, operations research, and epidemiology. By integrating methods and approaches from these diverse fields, he has produced foundational results on the design of disease surveillance networks. His work on surveillance was done in close collaboration with state and national-level public health agencies in the United States and has led to substantive changes in their surveillance practices. Sam believes that continuing to develop collaborations with public health decision makers is critical to preparing for and responding to future epidemics.

Sam earned a B.Sc. in biology from Indiana University Bloomington and a Ph.D. in integrative biology from The University of Texas at Austin. His dissertation research was supported by a National Science Foundation Graduate Research Fellowship and a Doctoral Dissertation Improvement Grant. He attended the Santa Fe Institute's Complex Systems Summer School in 2010.