Degree

M.S. Agroecology (2009); Ph.D. Agronomy (2016)

Education

M.Sc. Agroecology, University of Wisconsin, Madison, 2009

B.S. Biological Sciences, Stanford University, 2006

Current Position

Statistical Marketing Analyst – American Family Insurance, Madison, WI

Research and Professional Interests

I study the link between soil microbial communities and nitrous oxide (N₂O) production in bioenergy cropping systems. N₂O is a potent greenhouse gas that in the U.S. primarily stems from agricultural activities. N₂O is produced by soil microbes through processes that include nitrification (ammonia oxidation) and denitrification (nitrate reduction). Modeling and predicting N₂O production is a major challenge, particularly for ecologically complex systems like perennial grasslands. I am working with the giant trace gas dataset generated by the Great Lakes Bioenergy Research Center and with a large collection of shotgun metagenomes to correlate the microbial community to N₂O generation patterns.

Another aspect of my research considers the effects of nitrogen fertilization on nitrogen cycling in switchgrass fields. This is a collaborative project where my focus is on comparing denitrification and N₂O production under field conditions to potential denitrification under idealized conditions. By studying denitrification under conditions where it is limited only by the capabilities of the soil microbial community, I hope to improve our understanding of how much these capabilities vary and how they might influence the N₂O we actually observe in the field.

I also work with Zip-Lignin, a variety of lignin genetically engineered to be easier to break apart chemically. I am incubating tissues from Zip-Lignin poplars in soil microcosms to test whether these materials decompose more quickly in soil, which might impact soil carbon accumulation in systems with this trait.

My overarching interests entail understanding and modeling complex systems to improve our capacity to predict their future behavior. This makes me a voracious consumer of statistical methods, as I look for new ways to quantify and interpret variability in the systems I study. Mixed effects models (linear and nonlinear) and ordination are my workhorses, but I have been expanding into elastic net models, Bayesian models and model averaging. As shiny and enticing as they are, statistical methods alone are insufficient to fully understand a system. I make an effort to understand the biology and mechanisms that govern systems I study so that I will be able to contextualize and critically interpret my analyses. I also relish the opportunities, when they arise, to design experiments to explicitly test and further our understandings of these systems.