Toward stable, general machine- learned models of the atmospheric chemical system.” JGR: Atmospheres, 125, e2020JD032759. Publisher's VersionRead more. 2020. “
I am a fourth-year Ph.D. student working with Prof. Daniel Jacob in the Atmospheric Chemistry Modeling Group at Harvard University. The main goal of my current research is to use a machine learning framework to emulate atmospheric chemistry mechanisms for purposes of computational speedup and implementation into chemical transport models and Earth System Models, with additional applications for chemical data assimilation and short-term forecasting. Before coming to Harvard, I worked as a research assistant at the University of Washington with Prof. Julian Marshall on the application of machine learning methods to chemical mechanisms. I earned my B.A. in Chemistry from Reed College, with research experience pertaining to secondary organic aerosol (SOA) modeling using the chemical transport model GEOS-Chem. In my spare time, I watch horror films, enjoy basketball, and play jazz trombone.