The core focus of my research is the development and implementation of predictive and generative systems in healthcare settings. To enable code-to-clinic contributions, my research combines multiple disciplines including software engineering, deep learning, healthcare informatics, and implementation science. I completed my Bachelor of Science in Chemical Engineering at the University of California, Berkeley in 2014, my Master of Biotechnology Enterprise and Entrepreneurship at Johns Hopkins University in 2017, and my Master of Computer Science and PhD in Biomedical Informatics at the University of California, San Diego in 2024. I have over 9 years of industry experience in the biotechnology and medical technology sectors including experience at Genentech and Illumina. Some of my ongoing and recently completed research projects include development & deployment of real-time analytics in critical care settings, phenotyping and endotyping of sepsis patients, and automated assessment of quality measures. During my doctoral training I designed an interoperable, scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. On this foundation, I deployed a deep learning model for the prediction of sepsis into clinical practice across two emergency departments and observed a 17% reduction in sepsis-related mortality associated with the AI intervention. This work was featured in Nature Digital Medicine, Fortune, and KPBS, and represents the first study to demonstrate improvement in patient-centered outcomes attributable to the deployment of a deep-learning-based sepsis prediction model.