I work with complex data and difficult experimental conditions. On the computer, I program in Python supplemented with bash and R to interpret biological data, which is heterogenous – requiring an understanding of not just patterns in data but also the underlying biological processes. In the laboratory, I study not a single model organism in a specific growth condition but multiple non-model organisms in multiple growth conditions, often requiring special equipment to maintain oxygen-less atmospheres. Working with this mix of computational and laboratory methods has been both stimulating and rewarding for my research.
I am open-minded and a strong learner. I believe that people can and should improve. I participated in workshops on active listening, leadership, and mentorship to help me better contribute to teams, and I have taken courses in evidence-based teaching, science communication, and data visualization to learn how to communicate complex information to non-scientists. I am a regular reader of non-fiction in many topics and a devotee of Thinking, Fast and Slow, which explains biases in human thought.
I am focused and systematic. In extracurricular work, I paid forward an NSF graduate research fellowship by mentoring undergraduate students from non-traditional backgrounds and chairing a campus-wide student committee to address hunger and homelessness. In research, I have expanded my study system from a specific metabolism in single cultures to multiple metabolisms across entire communities. I am excited to apply what I have learned from these experiences to new teams.