My primary interests are in applied econometrics with applications related to genomics, nutrition, health, and the environment. I have a quantitative and analytical background in the areas of applied economics and statistical genetics. I leverage my training with experience in machine learning and predictive modeling using SAS, R, and Python to solve problems. I can understand and produce peer reviewed research and discuss the application with a scientist, sales representative, or the customer whose problem ultimately drives the analysis. I can code my own estimators, execute SQL queries, parse text files, and visualize a social network.