Graph databases are flexible and scalable data stores that can support a wide variety of use cases and analytics. Graphs allow analysts to combine multiple types of data and their relationships in a native representation. Through the Apache TinkerPop API, I use Gremlin to build scalable and portable analytics for solving data science problems and building applications to bring powerful capabilities to users.

Understanding the relationships between data points and data attributes can provide crucial insight in solving hard problems. Identifying significant relationships requires new and advanced methods for data modeling. Through advances like the Clique Tree and Galileo algorithms, I work on finding new ways to learn from data.