Stephanie Hong is the Director of Translational Informatics Application at the Biomedical Informatics and Data Science Section of Johns Hopkins School of Medicine. As the lead programmer for the National COVID Cohort Collaborative (N3C) data Ingestion and harmonization workstream, she designed the data pipeline and wrote the terminology transformation code to ingest EHR (electronic health record) data from 232 rural and urban medical institutions in their native formats – PCORnet, ACT, OMOP, TriNetX CDM datasets – and to harmonize the data into a common data model (CDM) based on the Observational Medical Outcomes Partnership (OMOP) 5.3.1 version. N3C data Enclave has now become a platform that provides researchers access to COVID-related patient-level data in OMOP CDM format with over 29 billion rows of information. It is the largest centralized repository of COVID-related patient-level EHR data in U.S. Using Privacy Preserving Record Linkage (PPRL) technology, N3C EHR data is now connected to environmental, claims, viral variant, social and other datasets. N3C has transformed how NIH and other federal agencies view big data, open science, and the power of cloud-based collaborative platforms. At Johns Hopkins, she also manages other ETL work, including the transformation of state designated Health Information Exchange of Maryland dataset into OMOP CDM instance for COVID research. She is interested in how to classify patient phenotypes, and how to harmonize and render real-world clinical data to support data inferencing for translational research. She is a MIT trained engineer and was inducted into as a fellow of American Medical Informatics Association in May 2022. She is a proud mother of her five kids.