Some projects I'm working on & think are beautiful/cool!


The evolutionary tree lurks behind all community ecological datasets as a tangled and ever growing classification scheme. The evolutionary tree defines sets of species - lineages - whose traits are often more similar with one-another than with other species.

The evolutionary can be a source of confounding correlations between species, but it can also serve as a scaffold to infer when & where important traits arose, thereby organizing how we understand biological systems.

How can we use this tree to simplify our analyses of communities, from tropical bird communities facing deforestation or viral communities infecting mammals, or bacterial communities called microbiomes?

We can cut it up, one branch at a time, sequentially defining relevant subsets hypothesized to share common traits.

Every branch in the tree separates our community into two groups: those below the branch, and those above the branch. By constructing variables that contrast these two groups of species, we can find the edges with the most-different species. Cutting the tree's branches simplifies large communities by summarizing them in terms of a few lineages instead of thousands of species. 

These inferences on the tree of life motivate further studies of microbial physiology and genome biology to understand what evolutionary event happened along the edges we found. 



All pathogens come from somewhere. From coronaviruses in bats, influenzas in swine and birds, Lyme disease in rodents & ticks, malaria in primates & mosquitoes, and Ebola in reservoirs we're currently unsure of, pathogens that are novel to humans have age-old relationships with other animals.

I'm interested in everything from spillover to human epidemics. From percolation models of the risk of spillover, clever tricks to use syndromic surveillance to quantify the size of outbreaks of pathogens like SARS-CoV-2 in the US, the evolution of new strains like B 1.1.7, and tools to compare the world's epidemics on timescales of death. The figure you see here shows the excess of patients visiting the doctor with influenza-like illness during the COVID-19 epidemic in 2020.

My work in epidemiology builds on my work in finance. High-stakes forecasting requires high-quality data streams, fast & robust analyses, and knowing when you know enough to act and when you don't.