Machine Learning and Data modeling scientist interested in socially relevant applications of data models. I am particularly interested in using the best practices to develop methods to improve and automate science in our technologically advancing world. As a postdoctoral researcher at Woods Hole Oceanographic Institution, I applied inverse modeling techniques to understand the dynamics and fluxes of organic carbon in the ocean. I work with Phoebe Lam and Olivier Marchal on projects that aim to quantify and predict the mechanistic controls of carbon cycling.I have over seven years of experience in ocean biogeochemistry research, with a PhD from UCLA and a BA from UC Berkeley. I have published multiple studies on various aspects of ocean biogeochemistry, focusing on nitrous oxide, carbon export, and harmful algal blooms. I have also developed and implemented machine learning pipelines to analyze large and complex ocean datasets, and to validate and improve ocean models. I am passionate about applying my skills and knowledge to address socially and environmentally relevant problems, and I am open to opportunities in academia or industry that align with my interests and goals