She is interested in bridging together quantum chemistry, stastistical thermochemistry, and transition state theory, to characterize atmospheric and combustion systems. Doing so on a large-scale, by developing automated workflow tools, allows for a better understanding of their processes and how to improve efficiency. She has been developing large databases of thermodynamic and kinetic parameters, upon which she is exploring the use of machine learning methods and cheminformatics to heighten the reliability of the modeling data. She also endeavors to present these results in a meaningful way -- with quantified uncertainties and made highly available.