A little bit about me

The tough part about being a mathematical biologist is that the mathematicians think you've gone soft and the biologists think you've gone crazy.

I am a mathematical biologist. I love developing novel mathematical tools motivated by biological problems, and I love using mathematics to interrogate the logical consequences of biological theories for more informative study design.

My main interests are in epidemiology, community ecology, community assembly, evolution/co-evolution, and stochastic processes. I'm also interested in human systems, such as financial markets and human memetic communities (the ideas floating around in our heads) as evolving systems with data complementary to what we can collect in biology.

My side interests are outdoor sports, philosophy, trading strategy development and portfolio management, and sipping whiskey by a fireplace.

MY LATEST RESEARCH

Phylofactorization - How can we simplify microbiome data using phylogenies? My research on phylogenetic factorization developed a new way to simplify biological big-data by constructing variables corresponding to edges in the tree of life. Above, we've characterized microbes in the American Gut project with a phylogenetic analog of principal components analysis, yielding phylogenetic components of variance in the gut microbiome.

Mapping our inferences onto the tree of life enables further studies of microbial physiology and genome biology to understand why microbes in different parts of the evolutionary tree have different habitat preferences.

Pathogen Spillover and Epidemic Forecasts - All pathogens come from somewhere. Bats, for example, harbor a range of viruses capable of wreaking havoc in human populations, from Henipaviruses to Coronaviruses. 

My work of late has spanned the entire process of spillover, emergence, and epidemics. From percolation models of the risk of spillover and phylodynamic models of pathogen evolution in wildlife reservoirs to predicting the probability of COVID "spilling over" from one country to another (the math - percolation models - is the same) and understanding what we can and can't forecast aboud epidemics of novel diseases as they unfold.

My work in epidemiology synergizes with my interests in finance - forecasting high-stakes games requires finding the most trustworthy data streams, fast and robust analyses, and, above all, knowing when you know enough to commit and when you don't. Stay tuned for more!