The Statistical Diversity Lab (est. 2017), is my research group in the Department of Biostatistics at the University of Washington. We apply our statistical training to develop methods for the analysis of biodiversity data, with a particular emphasis on the microbiome. Microbes play a critical role in a wide variety of human diseases and environmental outcomes, and manipulating the microbiome can be as easy as washing your hands or taking a probiotic. We believe that the microbiome is the most exciting frontier of human and environmental health research, and that's why it is the focus of our methods, theory and collaborative work.

My research background is in statistical inference on non-Euclidean metric spaces, discrete data models, applied probability, and hierarchical modelling. The more recent work of my group focuses on modeling relative abundance, diversity, phylogenetic uncertainty and batch effects, and developing statistical inference procedures for amplicon, whole-genome and metabolomic datasets. We believe that there are many un/underused data structures in microbial sequencing studies that have the potential to greatly improve microbiome modeling. We develop and sustain long-term collaborative relationships with outstanding microbial ecologists to keep our methodological and theoretical work relevant. Methodological development for microbial ecology demands expertise in compositional data analysis, networked data, high dimensional data, constrained estimation, and missing data, and we love learning from our colleagues in statistics, probability and applied math to expand our skills in these areas.

Science moves forward through an iterative process of positing hypotheses and using data to evaluate them. Evaluating the evidence for a hypothesis is often extremely complex, and specialised or custom methods are needed. The Statistical Diversity Lab develops specialised methods for the analysis of data collected for studying biodiversity.

In the 21st century, many scientists studying biodiversity are not directly observing the creatures they study. Often, we will be looking for their molecular signatures - inferring their presence and features, rather than directly observing them. These inferences about life contain both random and systematic error. As a result, much of our work focuses on mathematically describing the probability of these errors. These descriptions are called statistical models. We propose, construct and evaluate statistical models for biodiversity data, and apply them to data - ultimately allowing us to evaluate scientific hypotheses. A particular focus of our lab is the unseen world of bacteria. Communities of bacteria are everywhere, and bacteria play key roles in the human digestive and immune systems, as well as in food production and global climate cycles. Modern microbiological data is extremely complex, and that's why we find statistical models for this data so interesting and challenging to work with.